@prefix dcat: . @prefix dct: . @prefix schema: . @prefix ai4qc: . @prefix rdfs: . @prefix xsd: . a dcat:Dataset, schema:Dataset ; dct:title "MTARSI dataset"@en ; dct:description "MTARSI has a total of 9385 remote sensing images acquired from Google Earth satellite imagery and manually expanded. The dataset contains 20 different types of aircraft covering 36 airports. Each image contains one and only one complete aircraft."@en ; dct:license ; dct:created "2019-05-18" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "Aircraft detection" ; dct:creator "Hefei University, China" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "AI4SmallFarms dataset"@en ; dct:description "AI4SmallFarms is a dataset for crop field delineation in Southeast Asian smallholder farms. This data set allows researchers to test and benchmark machine learning methods to delineate agricultural field boundaries in polygon format."@en ; dct:license ; dct:created "2023-10-03" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Field delineation" ; dct:creator "Delft University of Technology", "University of Twente, Faculty of Geo-Information Science and Earth Observation" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Overhead-MNIST dataset"@en ; dct:description "The Overhead-MNIST dataset is an overhead and satellite analog to the classic MNIST Digit Recognizer dataset. The dataset contains JPG images and CSV files. It includes 28x28 pixel grayscale images, for a total of 10 classes."@en ; dct:created "2021-01-18" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "MNIST Benchmark" ; dct:creator "PeopleTec, Inc., Huntsville, Alabama, USA" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Field Delineation (France) dataset"@en ; dct:description "Field delineation is important in developing and managing agricultural-related policies. Given an input satellite image, the goal is to output the delineated boundaries between farm parcels, or the segmentation masks of farm parcels. The dataset consists of Sentinel-2 satellite imagery in France in 2017. Each satellite image comes along with the corresponding binary masks of boundaries and areas of farm parcels, which are grey scale images. The dataset consists of 1572 training samples, 198 vali"@en ; dct:license ; dct:created "2020-06-01" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Sentinel-2", "Field boundary" ; dct:creator "Stanford University" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "pixbox"@en . a dcat:Dataset, schema:Dataset ; dct:title "CV-BrCT dataset"@en ; dct:description "CV-BrCT stands for Cross-View Brazilian Construction Type. The dataset is composed of 24000 pairs of images, each one containing a street-level scene and a high-resolution aerial image. Those samples are labeled in 8 different classes."@en ; dct:created "2020-07-22" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "Multi-source inagery", "Urban image classification" ; dct:creator "Department of Computer Science, Universidade Federal de Minas Gerais, Brazil" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "GVLM dataset"@en ; dct:description "The Global Very-High-Resolution Landslide Mapping (GVLM) dataset is the first large-scale and open-source VHR landslide mapping dataset. It includes 17 bitemporal very-high-resolution imagery pairs. Each sub-dataset contains a pair of bitemporal images and the corresponding ground-truth map."@en ; dct:license ; dct:created "2023-02-07" ; ai4qc:taskType "change detection" ; dcat:keyword "change detection", "landslide" ; dct:creator "Wuhan University of Science and Technology", "Smart Cities Research Institute, The Hong Kong Polytechnic University", "Helmholtz Institute Freiberg for Resource Technology", "The Chinese University of Hong Kong" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Ships in Google Earth dataset"@en ; dct:description "This dataset contains 794 Google Earth images annotated with bounding boxes around ships."@en ; dct:created "2018-10-24" ; ai4qc:taskType "object detection" ; dcat:keyword "Object detection", "Ship detection" ; dct:creator "Tom Luther" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "RSD46-WHU dataset"@en ; dct:description "RSD46-WHU is a larget-scale open dataset for scene classification in remote sensing images. The dataset is collected from Google Earth and Tianditu. The ground resolution of most classes is 0.5m, and the others are about 2m. There are 500-3000 images in each class. The RSD46-WHU dataset contains around 117,000 images with 46 classes."@en ; dct:license ; dct:created "2019-04-30" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "Scene classification" ; dct:creator "Shenzhen Prafly Technology Co., Ltd., China", "State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "95-Cloud dataset"@en ; dct:description "95-Cloud dataset is an extensive dataset for evaluation of deep learning-based cloud segmentation models. 95-Cloud dataset is an extension of our previous 38-Cloud dataset. 95-Cloud has 57 more Landsat 8 scenes for training."@en ; dct:license ; dct:created "2021-01-14" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Landsat 8", "Cloud masks" ; dct:creator "Laboratory for Robotics Vision (LRV), School of Engineering Science at Simon Fraser University, Canada" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Hi-UCD dataset"@en ; dct:description "Hi-UCD (ultra-High Urban Change Detection) series datasets are designed for deep learning based urban semantic change detection. Both semantic labels and change labels are available."@en ; dct:license ; dct:created "2024-07-18" ; ai4qc:taskType "change detection" ; dcat:keyword "Change detection", "Land cover", "Urban change" ; dct:creator "Wuhan University" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Synthinel-1 dataset"@en ; dct:description "The Synthinel-1 dataset was created using an approach to generate large and diverse synthetic overhead imagery for training segmentation CNNs with CityEngine. The synthetic overhead imagery is associated with full pixel-wise building labels."@en ; dct:created "2020-01-15" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Building detection", "Synthetic imagery" ; dct:creator "Department of Electrical & Computer Engineering, Duke University", "Energy Initiative, Duke University" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Canadian Cropland dataset"@en ; dct:description "The Canadian Cropland Dataset is inspired by the Eurosat dataset. It is compiled using optical satellite images of Canadian agricultural croplands retrieved from Sentinel-2. It contains 78536 high-resolution geo-referenced images of 10 main crop types."@en ; dct:license ; dct:created "2021-09-30" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "Sentinel-2", "Crop types" ; dct:creator "Agriculture and Agri-Food Canada", "Laboratoire d’algèbre, de combinatoire et d’informatique mathématique, Université du Quebec à Montréal" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "USTC_SmokeRS dataset"@en ; dct:description "The dataset was created to detect the fire smoke in satellite imagery. Smoke detection based on satellite imagery is imperative for wildfire detection and monitoring."@en ; dct:created "2019-07-18" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "Smoke detection" ; dct:creator "State Key Laboratory of Fire Science, University of Science and Technology of China", "Department of Civil and Architectural Engineering, City University of Hong Kong", "Department of Electrical and Computer Engineering, University of North Carolina" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Weakly Supervised Cropland Segmentation Dataset for image classification"@en ; dct:description "This dataset supports weakly supervised cropland classification in the Midwestern United States. It contains single pixel-level and image-level labels derived from USDA land cover data, which provide high-quality reference labels across the US. This subset considers the image-level labels for the image classification task."@en ; dct:created "2020-01-07" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "Cropland" ; dct:creator "Stanford University" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "BigEarthNet-19 dataset"@en ; dct:description "BigEarthNet that is a new large-scale multi-label Sentinel-2 benchmark archive. The BigEarthNet consists of 590,326 Sentinel-2 image patches, each of which is a section of: 120x120 pixels for 10m bands; 60x60 pixels for 20m bands; and 20x20 pixels for 60m bands. Each image patch is annotated by multiple land-cover classes (i.e., multi-labels) that are provided from the CORINE Land Cover database. This version of the dataset has 19 different labels."@en ; dct:license ; dct:created "2019-02-16" ; ai4qc:taskType "multilabel classification" ; dcat:keyword "Land cover", "Multilabel classification" ; dct:creator "Technische Universität Berlin" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "3DCD dataset"@en ; dct:description "The 3DCD dataset contains pairs of optical images acquired in different epochs, provided together with both the related 2D change maps containing land use/land cover variations and the three-dimensional (3D) maps containing elevation changes."@en ; dct:license ; dct:created "2022-05-31" ; ai4qc:taskType "change detection" ; dcat:keyword "change detection", "3D task" ; dct:creator "Sapienza University of Rome" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Brazilian Coffee Scenes dataset"@en ; dct:description "This dataset is a composition of scenes taken by SPOT sensor in 2005. It has many intraclass variance caused by different crop management techniques. This dataset includes scenes with different plant ages and/or with spectral distortions caused by shadows."@en ; dct:created "2015-01-01" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "Land use" ; dct:creator "SAMSUNG Research Institute, Brazil", "Universidade Federal de Minas Gerais, Brazil" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "LEVIR-Ship dataset"@en ; dct:description "LEVIR-Ship is a large-scale tiny ship detection dataset under medium-resolution remote sensing images. Images are captured from multispectral cameras of GaoFen-1 and GaoFen-6 satellites with a spatial resolution of 16m. Only the R, G, and B bands were used. 85 scenes have been collected in the dataset with pixel resolutions between 10000×10000 and 50000×20000. The original images were cropped to finally get 1973 positive samples and 1923 negative samples with the size of 512×512."@en ; dct:license ; dct:created "2022-11-06" ; ai4qc:taskType "object detection" ; dcat:keyword "Ship detection", "GaoFen" ; dct:creator "Beihang University in Beijing, China" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "S2_New_Anomalies_AI4QC dataset"@en ; dct:description "This dataset was used in the AI4QC project (Artificial Intelligence for Quality Control), in the context of the detection of new anomalies through unsupervised learning. It consists of 6452 Sentinel-2 images (true color images in jpg format). The dataset was divided into training (80%) and testing (20%). Two criterias were considered for the train/test split: seasonality and geographic location."@en ; dct:license ; dct:created "2024-09-23" ; dct:modified "2024-10-09" ; ai4qc:taskType "unsupervised learning" ; dcat:keyword "sentinel-2", "unsupervised learning", "anomaly detection" ; dct:creator "BVLabs" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "SYSU-CD dataset"@en ; dct:description "The dataset contains 20000 pairs of 0.5-m aerial images. The main types of changes in the dataset include: newly built urban buildings; suburban dilation; groundwork before construction; change of vegetation; road expansion; sea construction."@en ; dct:created "2021-06-22" ; ai4qc:taskType "change detection" ; dcat:keyword "change detection" ; dct:creator "State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University", "Xinjiang Common University Key Laboratory of Smart City and Environmental Stimulation", "Guangdong Provincial Key Laboratory for Urbanization and Geo-Simulation, Sun Yat-sen University" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "TreeSatAI-TS dataset"@en ; dct:description "TreeSatAI-TS is a multimodal and multi-temporal dataset for tree species identification, containing 50381 tiles of 60x60 m with multi-label annotations for 20 classes."@en ; dct:license ; dct:created "2024-07-17" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "Tree species classification" ; dct:creator "CNRS, France", "IGN, France", "CNES, France" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "MSTAR-8class dataset"@en ; dct:description "The Moving and Stationary Target Acquisition and Recognition (MSTAR) was a joint Defense Advanced Research Projects Agency (DARPA) and Air Force Research Laboratory (AFRL) effort to develop and evaluate anadvanced ATR system. The program began in June of 1995 and ended in 1999. The MSTAR-8class dataset provides 12166 SAR image chips covering 8 target types from the former Soviet Union. The images were collected by the Sandia National Laboratory (SNL) using the STARLOS sensor."@en ; dct:created "1996-11-01" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "SAR", "Automatic target recognition" ; dct:creator "Air Force Research Laboratory", "Defense Advanced Research Projects Agency" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Representation Learning for Land Cover Classification dataset"@en ; dct:description "The Representation Learning for Land Cover Classification dataset from SustainBench is a large-scale dataset for land cover classification and representation learning, featuring global coverage with multi-spectral satellite imagery and 66 land cover classes."@en ; dct:license ; dct:created "2019-06-01" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "Land cover", "Representation learning", "Satellite imagery" ; dct:creator "Stanford University" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "OIRDS dataset"@en ; dct:description "The OIRDS project (Overhead Imagery Research Data Set) has produced a dataset with 904 labeled images suitable for developing automated vehicle detection algorithms. These images contain approximately 1800 labeled targets. For each target, the OIRDS provides over 30 annotations and over 60 statistics that describe the target within the context of the image."@en ; dct:created "2009-05-11" ; ai4qc:taskType "object detection" ; dcat:keyword "Object detection", "Vehicle detection" ; dct:creator "Colder Scientific", "Raytheon", "Booz Allen Hamilton", "National Geospatial-Intelligence Agency", "ImageryX Associates", "Northrup Grumman", "The Mitre Corporation" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "MUMUCD dataset"@en ; dct:description "The MUlti-modal MUlti-class Change Detection (MUMUCD) dataset provides bitemporal acquisitions obtained by different satellites. MUMUCD includes hyperspectral (visible and shortwave infrared) by the PRISMA satellite, multispectral (visible and shortwave infrared) by Sentinel-2 satellites and synthetic aperture radar magnitude information by Sentinel-1 satellites. Additional layers include bitemporal land cover data from the Dynamic World project and static digital elevation model data retrieved "@en ; dct:license ; dct:created "2025-06-19" ; ai4qc:taskType "change detection" ; dcat:keyword "Change detection", "Land cover" ; dct:creator "Institute of Marine Sciences, Rome", "Φ-Lab, European Space Agency", "DG CNECT, European Commission" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "OMBRIA dataset"@en ; dct:description "It is a Sentinel-1 and Sentinel-2 imagery dataset constructed for benchmarking the OmbriaNet deep learning CNN architecture. OmbriaNet was designed for adressing the flood mapping problem."@en ; dct:created "2021-06-11" ; ai4qc:taskType "change detection" ; dcat:keyword "Change detection", "Flood event" ; dct:creator "University of Crete" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "COWC Object Detection dataset"@en ; dct:description "The Cars Overhead With Context (COWC) data set is a large set of annotated cars from overhead. All cars in the annotated images have a dot placed at their center. Negative samples are also included in the dataset. It can be used for image classification or object detection/counting tasks. This schema describes the object detection subset of the dataset."@en ; dct:license ; dct:created "2016-01-01" ; ai4qc:taskType "object detection" ; dcat:keyword "Aerial imagery", "Car detection" ; dct:creator "Lawrence Livermore National Laboratory" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Sentinel-2 Cloud Cover Segmentation dataset"@en ; dct:description "This dataset was generated as part of crowdsourcing competition, and later on was validated using a team of expert annotators. The dataset consists of Sentinel-2 satellite imagery and corresponding cloudy labels stored as GeoTiffs. There are 22728 chips collected between 2018 and 2020."@en ; dct:license ; dct:created "2022-03-15" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Cloud masks", "Sentinel-2" ; dct:creator "Radiant Earth Foundation" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "S1S2-Water dataset"@en ; dct:description "S1S2-Water dataset is a global reference dataset for training, validation and testing of convolutional neural networks for semantic segmentation of surface water bodies. The dataset consists of 65 triplets of Sentinel-1 and Sentinel-2 images with quality checked binary water mask."@en ; dct:license ; dct:created "2023-09-04" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Sentinel-2", "Sentinel-1", "Water detection" ; dct:creator "German Aerospace Center", "German Research Centre for Geosciences" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "DLR-MVDA dataset"@en ; dct:description "DLR-MVDA (DLR Multi-class Vehicle Detection and orientation in Aerial imagery) is an open aerial image dataset with annotated vehicles captured from an airplane over the city of Munich, Germany."@en ; dct:license ; dct:created "2015-06-07" ; ai4qc:taskType "object detection" ; dcat:keyword "Object detection", "Vehicle detection" ; dct:creator "Remote Sensing Technology Institute, German Aerospace Center" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "FLAIR #2 dataset"@en ; dct:description "FLAIR stands for French Land cover from Aerospace ImageRy. It is composed of over 20 billion annotated pixels of very high resolution aerial imagery. Furthermore, to integrate broader spatial context and temporal information, high resolution Sentinel-2 1-year time series with 10 spectral band are also provided."@en ; dct:license ; dct:created "2023-06-19" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Multi-source imagery" ; dct:creator "Institut national de l'information geographique et forestiere, France" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "LandCover.ai dataset"@en ; dct:description "The LandCover.ai (Land Cover from Aerial Imagery) dataset is a dataset for automatic mapping of buildings, woodlands, water and roads from aerial images."@en ; dct:license ; dct:created "2020-05-05" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Land cover" ; dct:creator "Linux Polska, Warsaw", "Interdisciplinary Centre for Mathematical and Computational Modelling, University of Warsaw", "Agency for Restructuring and Modernisation of Agriculture, Warsaw" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Airbus Aircraft dataset"@en ; dct:description "The dataset was created to use deep learning to detect automatically the number, size and type of aircrafts present on the site. In turn, this can provide information about the activity of any airport. The dataset contains 103 images accompanied by aircraft bounding boxes."@en ; dct:license ; dct:created "2021-03-12" ; ai4qc:taskType "object detection" ; dcat:keyword "Object detection", "Aircraft detection" ; dct:creator "Jeff Faudi", "Airbus DS GEO S.A." ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "PatternNet dataset"@en ; dct:description "PatternNet is a large-scale remote sensing dataset that was collected specifically for Remote sensing image retrieval. PatternNet was collected from high-resolution imagery and contains 38 classes with 800 images per class."@en ; dct:created "2017-06-11" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "Image retrieval" ; dct:creator "State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University", "Electrical Engineering and Computer Science, University of California" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "BigEarthNet-43 dataset"@en ; dct:description "BigEarthNet that is a new large-scale multi-label Sentinel-2 benchmark archive. The BigEarthNet consists of 590,326 Sentinel-2 image patches, each of which is a section of: 120x120 pixels for 10m bands; 60x60 pixels for 20m bands; and 20x20 pixels for 60m bands. Each image patch is annotated by multiple land-cover classes (i.e., multi-labels) that are provided from the CORINE Land Cover database. This version of the dataset has 43 different labels."@en ; dct:license ; dct:created "2019-02-16" ; ai4qc:taskType "multilabel classification" ; dcat:keyword "Land cover", "Multilabel classification" ; dct:creator "Technische Universität Berlin" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "OPTIMAL-31 dataset"@en ; dct:description "The dataset was created for scene classification with images collected from Google Earth. It contains 31 classes and each class is formed by 60 images."@en ; dct:created "2018-09-05" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "Google Earth" ; dct:creator "Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, China", "Grenoble Images Speech Signals and Automatics Laboratory, Grenoble Institute of Technology", "Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Aerial Maritime Drone dataset"@en ; dct:description "This maritime object detection dataset contains 74 images of aerial maritime photographs taken with via a Mavic Air 2 drone. The data contains 1151 bounding boxes assigned to one of 5 classes."@en ; dct:license ; dct:created "2020-07-31" ; ai4qc:taskType "object detection" ; dcat:keyword "UAV imagery", "Maritime object detection" ; dct:creator "Jacob Solawetz" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "EnviroAtlas dataset"@en ; dct:description "This dataset contains imagery, land cover, and object layers for four of the cities covered by the EPA’s EnviroAtlas dataset: Pittsburgh, PA, Phoenix, AZ, Durham, NC, and Austin, TX. The dataset consists of 10 test tiles and 5 validation tiles in each city, with an additional 10 training tiles and 8 (total) validation tiles in Pittsburgh, subsetting the total area that the EnviroAtlas dataset covers in each city."@en ; dct:license ; dct:created "2021-12-13" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Land cover", "NAIP" ; dct:creator "University of California" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Satellite Buildings dataset"@en ; dct:description "The Kaggle Satellite Buildings Semantic Segmentation dataset contains 6038 images and masks for buildings detection through semantic segmentation."@en ; dct:license ; dct:created "2020-11-30" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Building detection" ; dct:creator "Wang" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "NeonTree dataset"@en ; dct:description "This dataset is the data files for the NeonTreeEvaluation Benchmark for individual tree detection from airborne imagery. For each geographic site, there are up to 4 files: a RGB image, a LiDAR tile, and a 426 band hyperpspectral file, and a 1m canopy height file. Annotatoins are bounding boxes around tree crowns."@en ; dct:license ; dct:created "2022-01-27" ; ai4qc:taskType "object detection" ; dcat:keyword "Object detection", "Airborne RGB", "Tree crowns" ; dct:creator "University of Florida" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "ERA dataset"@en ; dct:description "A dataset and deep Learning benchmark for event recognition in aerial videos. The dataset consists of 2864 videos each with a label from 25 different classes corresponding to an event unfolding 5 seconds."@en ; dct:created "2020-01-30" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "Event recognition" ; dct:creator "Remote Sensing Technology Institute, German Aerospace Center", "Signal Processing in Earth Observation, Technical University of Munich", "Technical University of Munich, Germany" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "RapidAI4EO dataset"@en ; dct:description "The RapidAI4EO corpus is a dataset of dense time series satellite imagery. At each location the corpus contains datacubes of two cloud-free, regular-cadence image products and corresponding land cover labels: Planet Fusion five-day cadence, Sentinel-2 L2A monthly image mosaics and CORINE Land Cover multiclass labels."@en ; dct:license ; dct:created "2024-06-26" ; ai4qc:taskType "multilabel classification" ; dcat:keyword "Land cover", "Multilabel classification", "Time series" ; dct:creator "RAPIDAI4EO" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "COWC-M Object Detection dataset"@en ; dct:description "The Cars Overhead With Context (COWC) data set is a large set of annotated cars from overhead. The COWC-M dataset extends the original COWC dataset by labeling the cars with types. Each car is now labeled as either Sedan, Pickup, Other or Unknown. It can be used for image classification or object detection tasks. This schema describes the object detection subset of the dataset."@en ; dct:license ; dct:created "2019-04-22" ; ai4qc:taskType "object detection" ; dcat:keyword "Aerial imagery", "Car detection" ; dct:creator "Lawrence Livermore National Laboratory" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "RSC11 dataset"@en ; dct:description "RSC11 Dataset is a remote sensing dataset designed for land cover classification using high-resolution satellite imagery. It contains 11 classes representing different land cover types. The dataset includes 1232 images in total, with each class about 100 images."@en ; dct:created "2016-07-12" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "Google Earth", "Urban and natural land cover" ; dct:creator "Institute of Remote Sensing and Digital Earth, China" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "AID dataset"@en ; dct:description "Aerial Image Dataset (AID) is a large-scale aerial image dataset generated by collecting sample images from Google Earth imagery. The goal of AID is to advance the state-of-the-arts in scene classification of remote sensing images. For creating AID, more than ten thousands aerial scene images have been collected and annotated."@en ; dct:created "2017-04-24" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "Land cover" ; dct:creator "Wuhan University", "Huazhong University of Science and Technology" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "HRSID dataset"@en ; dct:description "High resolution SAR images dataset (HRSID) is a dataset for ship detection, semantic segmentation, and instance segmentation tasks in high-resolution SAR images. This dataset contains a total of 5604 high-resolution SAR images and 16951 ship instances."@en ; dct:license ; dct:created "2020-06-29" ; ai4qc:taskType "object detection" ; dcat:keyword "Object detection", "SAR imagery", "Ship detection" ; dct:creator "University of Electronic Science and Technology of China" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "VALID Semantic Segmentation dataset"@en ; dct:description "VALID stands for Virtual Aerial Image Dataset. It consits of 6690 high-resolution images, all annotated with panoptic segmentation on 30 categories, object detection with the oriented bounding box, and binocular depth maps, collected in 6 different virtual scenes and 5 various ambient conditions (sunny, dusk, night, snow and fog). This subset considers the semantic segmentation task."@en ; dct:created "2020-01-27" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Aerial imagery" ; dct:creator "State Key Laboratory of Intelligent Technology and Systems, Tsinghua University" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "SpaceNet-6 dataset"@en ; dct:description "SpaceNet-6 is a multi-sensor all weather mapping dataset. It contains electro-optical imagery collected with different spatial resolutions: one band panchromatic (0.5m), 4 multi-spectral bands (2.0m): blue, green, red, and near-infrared (NIR) and 4 pan-sharpened bands (0.5m): blue, green, red, and NIR. The SAR data offers four polarizations (HH, HV,VH, and VV) of data in the X-band wavelength."@en ; dct:license ; dct:created "2020-04-14" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Building detection", "Road detection", "Multi-sensor" ; dct:creator "German Aerospace Center", "Maxar Technologies", "Intel AI Lab", "Capella Space", "In-Q-Tel - CosmiQ Works" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "WHU-OHS dataset"@en ; dct:description "The dataset consists of about 90 million manually labeled samples of 7795 Orbita hyperspectral satellite (OHS) image patches from 40 Chinese locations."@en ; dct:created "2022-10-28" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Hyperspectral imagery" ; dct:creator "Wuhan University, China" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "MLRSNet dataset"@en ; dct:description "MLRSNet is a multi-label high spatial resolution remote sensing dataset for semantic scene understanding with deep learning from the overhead perspective. It is composed of high-resolution optical satellite or aerial images. MLRSNet contains a total of 109,161 samples within 46 scene categories, and each image has at least one of 60 predefined labels."@en ; dct:license ; dct:created "2020-07-13" ; ai4qc:taskType "multilabel classification" ; dcat:keyword "Multilabel classification", "Scene classification" ; dct:creator "Beijing Key Laboratory of Environmental Remote Sensing and Digital Cities, Beijing Normal University", "School of Land Science and Technology, China University of Geosciences", "Department of Informatics and Telecommunications, National and Kapodestrian University of Athens", "College of Computer Science and Technology, Chongqing University of Posts and Telecommunications" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "OSCD dataset"@en ; dct:description "The Onera Satellite Change Detection dataset addresses the issue of detecting changes between satellite images from different dates. It comprises 24 pairs of multispectral images taken from the Sentinel-2 satellites between 2015 and 2018."@en ; dct:created "2018-11-04" ; ai4qc:taskType "change detection" ; dcat:keyword "Sentinel-2", "Change detection" ; dct:creator "DTIS, ONERA, Universite Paris-Saclay", "LTCI, Telecom ParisTech" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "WHU-RS19 dataset"@en ; dct:description "WHU-RS19 is a set of satellite images exported from Google Earth, which provides high-resolution satellite images up to 0.5 m. It contains 19 classes of meaningful scenes in high-resolution satellite imagery. For each class, there are about 50 samples. The image samples of the same class are collected from different regions in satellite images of different resolutions and then might have different scales, orientations and illuminations."@en ; dct:license ; dct:created "2010-03-28" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "High-resolution imagery" ; dct:creator "CNRS-LTCI, TELECOM ParisTech", "Signal Processing Lab., Wuhan University" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "BioMassters dataset"@en ; dct:description "BioMassters is a deep learning ready dataset to predict yearly Above Ground Biomass (AGB) for Finnish forests using multi-temporal satellite imagery from the European Space Agency and European Commission's joint Sentinel-1 and Sentinel-2 satellite missions."@en ; dct:license ; dct:created "2023-08-20" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Above Ground Biomass estimation" ; dct:creator "University of Montreal and Novosibirsk State University", "Geoinformatics Division, KTH Royal Institute of Technology", "Geomatics Unit, University of Liège", "Driven Data", "Data Science Institute, Imperial College of London", "Dendra Systems" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "PlanesNet dataset"@en ; dct:description "PlanesNet is a labeled dataset consiting of image chips extracted from Planet satellite imagery. The purpose of PlanesNet is to serve as labeled training data to train machine learning algorithms to detect the locations of airplanes in Planet's medium resolution remote sensing imagery. The dataset includes 32000 20x20 RGB images labeled with either a 'plane' or 'no-plane' classification. Image chips were derived from PlanetScope full-frame visual scene products, which are orthorectified to a 3 m"@en ; dct:license ; dct:created "2020-08-15" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "Plane detection" ; dct:creator "Robert Hammell" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Forest Damages Larch Casebearer - Tree damage dataset"@en ; dct:description "The dataset was created to monitor forest areas affected by the larch casebearer, which is a moth that mainly attacks larch trees and has caused significant damage in Sweden. The original dataset contains 1543 images taken from two drone flying occasions over five affected areas in Västergötland, Sweden. The data set is structured in 10 batches, numbered 1 to 10. This subset of the dataset contains the batches 1 to 5, with labels providing the damage level of the Larch trees."@en ; dct:license ; dct:created "2021-04-01" ; ai4qc:taskType "object detection" ; dcat:keyword "Object detection", "Tree damage detection" ; dct:creator "Swedish Forest Agency" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Things And Stuff dataset"@en ; dct:description "This dataset was used to develop the things and stuff (TAS) context model."@en ; dct:created "2008-08-15" ; ai4qc:taskType "object detection" ; dcat:keyword "Object detection", "Vehicle detection" ; dct:creator "Department of Computer Science, Stanford University" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "FireRisk dataset"@en ; dct:description "FireRisk is a remote sensing dataset for fire risk assessment. It contains 7 fire risk classes with a total of 91872 labelled images."@en ; dct:created "2023-09-10" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "Fire risk assessment" ; dct:creator "University of Melbourne, Australia" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "WHDLD dataset"@en ; dct:description "WHDLD (Wuhan Dense Labeling Dataset) is a dense labeling dataset that can be used for multi-label tasks such as remote sensing image retrieval (RSIR) and classification, and other pixel-based tasks like semantic segmentation."@en ; dct:created "2018-06-14" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Dense labeling dataset" ; dct:creator "State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University", "Nanjing University of Information Science and Technology" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Post-Hurricane dataset"@en ; dct:description "The dataset contains satellite images of Texas after the Hurricane Harvey. It is divided into two groups: damage and no_damage. It was created to automatically identify if a given region is likely to contain flooding damage."@en ; dct:license ; dct:created "2019-11-15" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "Hurricane damage detection" ; dct:creator "Department of Industrial and Systems Engineering, University of Washington" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Urban Drone Dataset (UDD-6)"@en ; dct:description "This is a collection of drone images collected at Peking University, Huludao city, Henan University and Cangzhou city. Urban Drone Dataset encompasses UDD-5 and UDD-6. UDD-6 contains 141 images and ground truths."@en ; dct:license ; dct:created "2020-06-28" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Drone images" ; dct:creator "Department of Computer Science and Technology, Peking University, China" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "DLRSD-multilabel dataset"@en ; dct:description "DLRSD (Dense Labeling Remote Sensing Dataset) is a dense labeling dataset that can be used for multi-label tasks such as remote sensing image retrieval (RSIR) and classification, and other pixel-based tasks like semantic segmentation. It has 21 broad categories with 100 images per class, and 17 segmentation classes. This subset considers the multi-label classification task."@en ; dct:created "2018-06-14" ; ai4qc:taskType "multilabel classification" ; dcat:keyword "Multilabel classification", "Dense labeling dataset" ; dct:creator "State Key Laboratory of Information Engineering in Surveying, Wuhan University" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "EGY-BCD dataset"@en ; dct:description "The EGY-BCD dataset contains high-resolution and multitemporal satellite images captured in four urban and coastal areas in Egypt to detect building changes."@en ; dct:created "2023-06-07" ; ai4qc:taskType "change detection" ; dcat:keyword "change detection", "building change" ; dct:creator "State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "CLRS dataset"@en ; dct:description "This is a database designed for the task named Continual/Lifelong learning for remote sensing image scene classification. The proposed CLRS data set consists of 15,000 remote sensing images divided into 25 scene classes."@en ; dct:created "2020-04-01" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass scene classification" ; dct:creator "School of Geosciences and Info-Physics, Central South University, China", "School of Tourism and Geography, Yunnan Normal University", "Hefei Institutes of Physical Science, Chinese Academy of Sciences", "China Academy of Launch Vehicle Technology Research and Development Center" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "MSOSCD dataset"@en ; dct:description "The multisource OSCD (MSOSCD) dataset contains multispectral, SAR, and VHR multisource data for change detection. It is reformed from OSCD dataset."@en ; dct:license ; dct:created "2022-06-08" ; ai4qc:taskType "change detection" ; dcat:keyword "change detection", "multisource imagery" ; dct:creator "Division of Landscape Architecture, University of Hong Kong", "Guangdong Provincial Key Laboratory of Urbanization and Geo-Simulation, Sun Yat-sen University" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "HRC_WHU dataset"@en ; dct:description "HRC_WHU is a high-resolution cloud detection dataset. It contains 150 images and masks."@en ; dct:created "2018-11-13" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Cloud detection" ; dct:creator "School of Resource and Environmental Sciences, Wuhan University" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "WHU-OPT-SAR dataset"@en ; dct:description "WHU-OPT-SAR is the first and largest land use classification dataset that has fused high resolution optical and SAR images with sufficient annotation."@en ; dct:created "2021-06-12" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Land use" ; dct:creator "Wuhan University, China" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "PASTIS-HD dataset"@en ; dct:description "PASTIS-HD is a high-resolution multimodal benchmark dataset for semantic segmentation of agricultural parcels from satellite image time series. It combines 10m-resolution Sentinel-2 optical bands, SAR (VV and VH) from Sentinel-1, and very high-resolution (VHR) SPOT 6-7 RGB imagery. Each instance includes a time series with multi-sensor signals and corresponding semantic labels."@en ; dct:license ; dct:created "2024-04-12" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Agriculture", "Time series", "Multimodal" ; dct:creator "CNRS", "CNES", "IGN", "Univ Gustave Eiffel" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "BrazilDAM dataset"@en ; dct:description "BrazilDAM is a multi sensor and multitemporal dataset that consists of multispectral images of ore tailings dams throughout Brazil. Images are from Sentinel-2 and Landsat 8 satellites."@en ; dct:license ; dct:created "2020-01-27" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Landsat 8", "Multiclass classification", "Sentinel-2", "Dam detection" ; dct:creator "Department of Computer Science, Universidade Federal de Minas Gerais, Brazil", "Universidade Católica de Brasília and Tribunal de Contas da União, Brazil" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "SEN12MS dataset"@en ; dct:description "The SEN12MS dataset contains 180,662 patch triplets of corresponding Sentinel-1 dual-pol SAR data, Sentinel-2 multi-spectral images, and MODIS-derived land cover maps."@en ; dct:license ; dct:created "2019-09-16" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Sentinel-2", "Land cover", "Sentinel-1", "MODIS" ; dct:creator "Technical University of Munich" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "OpenSARShip dataset"@en ; dct:description "The dataset was created for Sentinel-1 ship interpretation. The version 1 of the dataset contains 11346 SAR ship chips for 41 Sentinel-1 SAR images. Version 2 adds 34528 SAR ship chips for 46 Sentinel-1 SAR images."@en ; dct:created "2017-10-13" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "Ship detection", "Sentinel-1" ; dct:creator "Shanghai Key Laboratory of Intelligent Sensing and Recognition" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "WHUS2-CD+ dataset"@en ; dct:description "WHUS2-CD+ is a cloud validation detection dataset for Sentinel-2A images. WHUS2-CD+ contains 36 manually labeled cloud masks at 10m resolution and corresponding Sentinel-2A images evenly distributed over China mainland."@en ; dct:created "2021-09-16" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Cloud detection" ; dct:creator "Wuhan University", "German Aerospace Center", "VTT Technical Research Centre of Finland", "Hangzhou Power Supply Company", "Shenzhen University" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Airbus Oil Storage dataset"@en ; dct:description "Aboveground Petroleum, Oil and Lubricant (POL) storage areas are common in the manufacturing industry and government facilities. Deep Learning can be used to detect automatically the number, size and type of POL storage present on a site. This can help monitoring the state of aboveground fuel storage tanks. This dataset contains 98 images with the corresponding bounding boxes around storage tanks."@en ; dct:license ; dct:created "2021-05-04" ; ai4qc:taskType "object detection" ; dcat:keyword "Object detection", "Oil storage tank" ; dct:creator "Jeff Faudi", "Airbus DS GEO S.A." ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "DynamicEarthNet dataset"@en ; dct:description "The DynamicEarthNet dataset that contains daily, cloud-free satellite data acquired from January 2018 to December 2019. For each of the 75 regions, the data contains a sequence of images with daily revisits. Pixel-wise semantic labels are available for the first day of each month. These serve as ground-truth to define land cover changes over the span of two observed years."@en ; dct:license ; dct:created "2022-03-22" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Land cover change" ; dct:creator "Technical University of Munich", "German Aerospace Center", "Planet Labs" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "CaBuAr dataset"@en ; dct:description "CaBuAr (California Burned Areas dataset) dataset contains pre- and post-fire images from Sentinel-2 satellites, acquired over burned regions in California. The ground truth masks are provided by the California Department of Forestry and Fire Protection and are aligned with the imagery."@en ; dct:license ; dct:created "2024-01-21" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Sentinel-2", "Forest fires" ; dct:creator "Politecnico di Torino, Turin, Italy" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Water Bodies dataset"@en ; dct:description "The Water Bodies dataset contains images and masks of satellite images of water bodies for image segmentation. The dataset contains 5682 images captured by Sentinel-2."@en ; dct:license ; dct:created "2020-05-18" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Sentinel-2", "Water detection" ; dct:creator "Francisco Escobar" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Kaggle Semantic Segmentation dataset"@en ; dct:description "The dataset was published by Humans in the Loop for a joint project with the Mohammed Bin Rashid Space Center in Dubai, the UAE. It consists of aerial imagery of Dubai obtained by MBRSC satellites and annotated with pixel-wise semantic segmentation in 6 classes. The total volume of the dataset is 72 images grouped into 8 larger tiles."@en ; dct:license ; dct:created "2020-05-29" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Aerial imagery" ; dct:creator "Humans in the Loop" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Vaihingen dataset"@en ; dct:description "The dataset was created to provide a benchmark for urban semantic segmentation. It was used in the 2D Semantic Labeling Contest. The data set contains 33 patches (of different sizes) acquired over Vaihingen (Germany), each consisting of a true orthophoto (TOP) extracted from a larger TOP mosaic and a DSM. The dataset is a subset of the data used for the test of digital aerial cameras carried out by the German Association of Photogrammetry and Remote Sensing (DGPF)."@en ; dct:created "2012-08-25" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Urban semantic segmentation", "Digital aerial images" ; dct:creator "German Association of Photogrammetry and Remote Sensing (DGPF)" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Planet UAS dataset"@en ; dct:description "THe Planet UAS dataset was created by Planet - designer and builder of the world's largest constellation of Earth-imaging satellites. The aim is to label satellite image chips with atmospheric conditions and various classes of land cover/land use. Resulting algorithms will help the global community better understand where, how, and why deforestation happens all over the world."@en ; dct:license ; dct:created "2017-07-21" ; ai4qc:taskType "multilabel classification" ; dcat:keyword "Multilabel classification", "Deforestation" ; dct:creator "Planet" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Potsdam dataset"@en ; dct:description "The dataset was created to provide a benchmark for urban semantic segmentation. The data set contains 38 patches (of the same size), each consisting of a true orthophoto (TOP) extracted from a larger TOP mosaic and a DSM."@en ; dct:created "2012-07-20" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Urban scene" ; dct:creator "International Society for Photogrammetry and Remote Sensing - ISPRS" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "NASA Marine Debris dataset"@en ; dct:description "The Marine Debris Dataset for Object Detection in Planetscope Imagery consists of 739 images of marine debris. The corresponding labels are bounding boxes with geographical coordinates. The images were obtained from PlanetScope optical imagery. In this dataset, marine debris consists of floating objects on the ocean surface which can belong to one or more classes namely plastics, algae, sargassum, wood, and other artificial items."@en ; dct:license ; dct:created "2023-10-10" ; ai4qc:taskType "object detection" ; dcat:keyword "object detection", "marine debris" ; dct:creator "NASA" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "38-Cloud dataset"@en ; dct:description "This dataset contains 38 Landsat 8 scene images and their manually extracted pixel-level ground truths for cloud detection. The entire images of these scenes are cropped into multiple 384*384 patches to be proper for deep learning-based semantic segmentation algorithms. There are 8400 patches for training and 9201 patches for testing."@en ; dct:license ; dct:created "2020-07-01" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Cloud mask", "Semantic segmentation", "Landsat 8" ; dct:creator "Laboratory for Robotics Vision (LRV), School of Engineering Science at Simon Fraser University, Canada" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "ShipsNet dataset"@en ; dct:description "The aim of this dataset is to automate the difficult task of detecting the location of large ships in satellite images. The dataset consists of images extracted from Planet satellite imagery collected over the San Francisco Bay and San Pedro Bay areas of California. It includes 4000 80x80 RGB images labeled with either a 'ship' or 'no-ship' classification. Images were derived from PlanetScope full-frame visual scene products, which are orthorectified to a 3-meter pixel size."@en ; dct:license ; dct:created "2018-07-30" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "Ship detection" ; dct:creator "Robert Hammell" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Aerial Cactus dataset"@en ; dct:description "The dataset was created for a Kaggle competition, which aims at aerial cactus identification. It is a modified version of the 'Cactus Aerial Photos' dataset, where images have been resized."@en ; dct:license ; dct:created "2019-07-09" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "Cactus detection" ; dct:creator "Kaggle" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "QuakeSet dataset"@en ; dct:description "QuakeSet is a dataset to analyze different attributes of earthquakes. It contains bi-temporal time series of images and ground truth annotations for magnitudes, hypocenters, and affected areas. The images are taken from the Sentinel-1 mission using the Interferometric Wide swath mode. The International Seismological Centre provides information about earthquakes."@en ; dct:license ; dct:created "2024-01-09" ; ai4qc:taskType "change detection" ; dcat:keyword "change detection", "earthquake detection" ; dct:creator "Politecnico di Torino" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "L7 Irish dataset"@en ; dct:description "This collection contains 206 Pre-Collection Landsat 7 Enhanced Thematic Mapper Plus (ETM+) Level-1G scenes. Manually generated cloud masks are used to train and validate cloud cover assessment algorithms, which in turn are intended to compute the percentage of cloud cover in each scene."@en ; dct:license ; dct:created "2015-11-23" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Cloud validation masks", "Landsat 7" ; dct:creator "U.S. Geological Survey" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "SpaceNet 1 dataset"@en ; dct:description "SpaceNet 1 is the first dataset released in the SpaceNet challenge series (2016). It contains high-resolution WorldView-2 satellite imagery and corresponding building footprint labels for areas over Rio de Janeiro, Brazil."@en ; dct:license ; dct:created "2016-09-01" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Building detection" ; dct:creator "In-Q-Tel CosmiQ Works", "Radiant Solutions" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "m-pv4ger-seg dataset"@en ; dct:description "M-pv4ger-seg dataset is a modified version of pv4ger-seg, adapted to Geo-Bench. It consists of the semantic segmentation equivalent of m-pv4seg. It is designed for rooftop-mounted photovoltaic systems detection."@en ; dct:license ; dct:created "2023-08-23" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Solar photovoltaic detection" ; dct:creator "Technical University of Munich", "ETH Zurich", "University of Maryland", "Stanford University", "Massachusetts Institute of Technology", "University of Amsterdam", "ServiceNow Research", "University of Montreal", "Clark University" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "SAT-4 dataset"@en ; dct:description "Images were extracted from the National Agriculture Imagery Program (NAIP) dataset. SAT-4 consists of a total of 500,000 image patches covering four broad land cover classes."@en ; dct:license ; dct:created "2015-09-11" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "NAIP" ; dct:creator "NASA Advanced Supercomputing Division, NASA Ames Research Center", "Department of Computer Science, Louisiana State University", "Bay Area Environmental Research Institute, NASA Ames Research Center" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "SWIM dataset"@en ; dct:description "SWIM-Ship Wake Imagery Mass dataset is a benchmark dataset built for deep learning-based ship wake detection. The objects to be detected include the wakes of ships, ranging from small yacht to huge container ships, and the backgrounds include open seas, ports, straits, and canals. Ship wakes are annotated with oriented bounding boxes."@en ; dct:license ; dct:created "2021-09-17" ; ai4qc:taskType "object detection" ; dcat:keyword "Object detection", "Ship wake" ; dct:creator "School of Optics and Photonics, Beijing Institute of Technology" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "HRSCD dataset"@en ; dct:description "High Resolution Semantic Change Detection Dataset (HRSCD) contains 291 coregistered image pairs of RGB aerial images from IGS's BD ORTHO database. Pixel-level change and land cover annotations are provided."@en ; dct:license ; dct:created "2019-10-01" ; ai4qc:taskType "change detection" ; dcat:keyword "Change detection", "Land cover" ; dct:creator "LTCI, Télécom ParisTech", "DTIS, ONERA, Université Paris-Saclay" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Out-of-Domain Land Cover dataset"@en ; dct:description "The dataset contains data from 692 regions of size 10km x 10km around the globe; for each region, 500 latitude/longitude coordinates are sampled for their satellite time series and land cover type."@en ; dct:created "2020-09-26" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "Land cover", "Time series" ; dct:creator "Technical University of Munich", "Stanford University" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Bandon dataset"@en ; dct:description "The building change detection with off-nadir aerial images dataset (BANDON) was built to address the problem of accurate detection and localization of urban building changes from oblique drone images. The BANDON dataset contains 2283 pairs of images, 2283 change labels, 1891 BT-flows labels, 1891 pairs of segmentation labels, and 1891 pairs of ST-offsets labels (test sets do not provide auxiliary annotations)."@en ; dct:created "2023-09-14" ; ai4qc:taskType "change detection" ; dcat:keyword "change detection", "building change" ; dct:creator "Wuhan University", "SenseTime Research, Beijing" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "VHR10 dataset"@en ; dct:description "This very-high-resolution (VHR) remote sensing image dataset was constructed by Dr. Gong Cheng et al for object detection tasks. These images were cropped from Google Earth and Vaihingen data set and then manually annotated by experts."@en ; dct:license ; dct:created "2016-10-18" ; ai4qc:taskType "object detection" ; dcat:keyword "Object detection", "VHR images" ; dct:creator "Northwestern Polytechnical University (NWPU)" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "GeoNRW dataset"@en ; dct:description "This dataset consists of orthorectified aerial photographs, LiDAR derived digital elevation models and segmentation maps with 10 classes, acquired through the open data program of the German state North Rhine-Westphalia and refined with OpenStreeMap."@en ; dct:license ; dct:created "2022-05-17" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Land cover" ; dct:creator "University of Tokyo", "RIKEN AIP", "INRIA", "Munich University of Applied Sciences" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "SynthWakeSAR dataset"@en ; dct:description "SynthWakeSAR is a synthetic ship classification dataset of synthetic aperture radar (SAR) images of the sea surface. It includes 10 real ship models for a total of 46080 simulated images containing visible ship wakes. The dataset was created using the 'AssenSAR Image Simulator', also available via the University of Bristol."@en ; dct:license ; dct:created "2020-08-03" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "Ship detection", "Synthetic imagery" ; dct:creator "University of Hull", "University of Bristol" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "GTA-V-SID dataset"@en ; dct:description "GTA-V-SID is a synthetic dataset for remote sensing image segmentation based on the well-known video game Grand Theft Auto-V (GTA-V). It is tailored for building extraction."@en ; dct:created "2020-02-17" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Building detection" ; dct:creator "Beihang University", "University of Michigan" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Cactus Aerial Photos dataset"@en ; dct:description "The dataset was created to develop a system for autonomous surveillance of protected areas. It contains more than 16000 examples of a columnar cacti (Neobuxbaumia tetetzo) for plant recognition or classification."@en ; dct:license ; dct:created "2019-05-10" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "Cactus recognition" ; dct:creator "Consejo Nacional de Ciencia y Tecnología (CONACYT)", "Universidad de la Cañada (UNCA)", "Instituto Politécnico Nacional (IPN), CIDETEC" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "AIR-PolSAR-Seg dataset"@en ; dct:description "The AIR-PolSAR-Seg dataset is a challenging PolSAR terrain segmentation dataset with high data scale and scene complexity. It contains 500 full-polarization SAR images and each image has 4 kinds of polarization modes (HH, HV, VH and VV). Therefore, the total number is 2000. These full-polarization SAR images are provided by the Gaofen-3 satellite at quad-polarized strip I (QPSI) mode."@en ; dct:created "2021-12-30" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "SAR imagery" ; dct:creator "Aerospace Information Research Institute, Chinese Academy of Sciences", "School of Electronic and Information Engineering, Soochow University", "University of Chinese Academy of Sciences" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "LoveDA dataset"@en ; dct:description "The Remote Sensing Land-Cover dataset for Domain Adaptive Semantic Segmentation (LoveDA) encompasses two scenes (urban and rural) with significant challenges arise from the presence of multi-scale objects, intricate background elements, and uneven class distributions within the dataset."@en ; dct:license ; dct:created "2021-10-15" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Land cover" ; dct:creator "State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "ORSSD dataset"@en ; dct:description "The ORSSD dataset consists of 800 optical remote sensing images with corresponding pixel-level annotations for salient object detection."@en ; dct:created "2019-05-03" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Salient object detection", "Optical remote sensing imagery" ; dct:creator "Beijing Jiaotong University", "City University of Hong Kong", "Beijing Key Laboratory of Advanced Information Science and Network Technology", "School of Electrical and Information Engineering, Tianjin University" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "PixBox_AI4QC dataset"@en ; dct:description "The dataset was used in the AI4QC project (Atrificial Intelligence for Quality Control), in the context of cloud detection. It originates from the PixBox-S2-CMIX dataset, which was used as a validation reference within the first Cloud Masking Inter-comparison eXercise (CMIX) in 2019. The PixBox-S2-CMIX pixel collection was conducted already in 2018. The PixBox_AI4QC dataset was derived from this PixBox dataset, where the cloud masks were reclassified and rasterized to TIFF files. The new classes"@en ; dct:license ; dct:created "2024-04-16" ; dct:modified "2024-04-16" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "cloud mask", "sentinel-2", "pixel collection" ; dct:creator "BVLabs" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "VALID Object Detection dataset"@en ; dct:description "VALID stands for Virtual Aerial Image Dataset. It consits of 6690 high-resolution images, all annotated with panoptic segmentation on 30 categories, object detection with the oriented bounding box, and binocular depth maps, collected in 6 different virtual scenes and 5 various ambient conditions (sunny, dusk, night, snow and fog). This subset considers the object detection task."@en ; dct:created "2020-01-27" ; ai4qc:taskType "object detection" ; dcat:keyword "Object detection", "Aerial imagery" ; dct:creator "State Key Laboratory of Intelligent Technology and Systems, Tsinghua University" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Car Parking Lot (CARPK) dataset"@en ; dct:description "The Car Parking Lot Dataset (CARPK) contains nearly 90,000 cars from 4 different parking lots collected by means of drone (PHANTOM 3 PROFESSIONAL). The images are collected with the drone-view at approximate 40 meters height. Besides, the dataset contains complete annotations for all cars in a single image from partial PUCPR dataset, called PUCPR+ dataset, which also has nearly 17,000 cars in total. This is a large-scale dataset focus for car counting in the scenes of diverse parking lots."@en ; dct:license ; dct:created "2017-07-19" ; ai4qc:taskType "object detection" ; dcat:keyword "Object detection", "Car park" ; dct:creator "National University of Taiwan" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Hephaestus for image classification dataset"@en ; dct:description "Hephaestus is a large scale multitask dataset created for InSAR (Interferometric SAR) development. It revolves around the 44 most active volcanoes globally and is focused around global volcano monitoring. This description focuses on the image classification task."@en ; dct:license ; dct:created "2023-07-03" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "InSAR imagery", "Volcano monitoring" ; dct:creator "National Observatory of Athens", "Harokopio University of Athens" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "MSBC dataset"@en ; dct:description "The multisource built-up change (MSBC) dataset contains multispectral, SAR, and VHR multisource data for change detection."@en ; dct:license ; dct:created "2022-06-08" ; ai4qc:taskType "change detection" ; dcat:keyword "change detection", "multisource imagery" ; dct:creator "Division of Landscape Architecture, University of Hong Kong", "Guangdong Provincial Key Laboratory of Urbanization and Geo-Simulation, Sun Yat-sen University" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "WHUAerial dataset"@en ; dct:description "The WHU building dataset is a multisource building extraction dataset providing high-quality labels, which consists of three subsets. Among these subsets, the aerial imagery subset is the most widely used for building extraction in the remote sensing community."@en ; dct:license ; dct:created "2018-08-22" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Building detection" ; dct:creator "School of Remote sensing and Information Engineering, Wuhan University", "Department of Physical Geography, Utrecht University" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Parallax_AI4QC dataset"@en ; dct:description "This dataset was used in the AI4QC project (Artificial Intelligence for Quality Control), in the context of parallax detection through an object detection task. It consists of a set of labeled parallax artefacts. This effect appears as a colorful pattern in images where cloud/heavy haze is present. Bounding boxes were defined around parallax artefacts in 1764 Sentinel-2 true colour images (jpg format). A parallax artefact appears mostly over clouds, although it can also be found over land. Two s"@en ; dct:license ; dct:created "2024-10-08" ; dct:modified "2024-10-08" ; ai4qc:taskType "object detection" ; dcat:keyword "object detection", "sentinel-2", "parallax" ; dct:creator "BVLabs" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "RSSCN7 dataset"@en ; dct:description "The RSSCN7 dataset is designed for scene classification tasks and provides a collection of high-resolution RGB images."@en ; dct:license ; dct:created "2017-12-30" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "Scene classification" ; dct:creator "Wuhan University" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "DeepGlobe Land Cover dataset"@en ; dct:description "The dataset comes from the DeepGlobe Land Cover Classification Challenge which offers high-resolution sub-meter satellite imagery focusing on rural areas. DeepGlobe Land Cover Classification Challenge dataset contains 1,0146 satellite images, split into training/validation/test sets, each with 803/171/172 images (corresponding to a split of 70%/15%/15%). All images contain RGB data collected from the DigitalGlobe Vivid+ dataset. Each satellite image is paired with a mask image for land cover ann"@en ; dct:license ; dct:created "2018-05-17" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Land cover" ; dct:creator "DigitalGlobe", "The MIT Media Lab", "Wageningen University", "Facebook", "CosmiQ Works" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "S2Looking dataset"@en ; dct:description "S2Looking is a building-change-detection dataset that contains large-scale side-looking satellite images captured at various off-nadir angles. The dataset consists of 5000 bitemporal pairs of very-high-resolution (VHR) images."@en ; dct:license ; dct:created "2021-12-27" ; ai4qc:taskType "change detection" ; dcat:keyword "change detection", "building change" ; dct:creator "Image Processing Center, Beihang University", "Institute of Resource and Environment, Capital Normal University", "Beijing Institute of Remote Sensing", "School of Microelectronics, Tianjin University" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Five Billion Pixels dataset"@en ; dct:description "Five Billion Pixels is a large-scake land cover classification dataset. It covers areas of more than 50,000 square kilometers in China, and contains more than 5 billion labeled pixels. It possesses the advantage of rich categories, large coverage, wide distribution, and high-spatial resolution, which well reflects the distributions of real-world ground objects and can benefit to different land cover related studies."@en ; dct:created "2023-01-10" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Land cover" ; dct:creator "Wuhan University", "Technical University of Munich", "German Aerospace Center" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "SSL4EO-S12-ML dataset"@en ; dct:description "SSL4EO-S12-ML dataset is a large-scale multi-label land cover land use classification dataset derived from SSL4EO-S12 images and Dynamic World segmentation maps. It consists of 780,371 multispectral Sentinel-2 images divided into 247,377 non-overlapping scenes each with 1-4 multi-seasonal patches. Each image has a multi-label annotation from one or more categories in 9 land cover land use classes."@en ; dct:license ; dct:created "2024-06-03" ; ai4qc:taskType "multilabel classification" ; dcat:keyword "Sentinel-2", "Land cover", "Multilabel classification" ; dct:creator "Technical University of Munich", "German Aerospace Center", "Munich Center for Machine Learning" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "SAT-6 dataset"@en ; dct:description "SAT-6 consists of a total of 405,000 image patches each of size 28x28 and covering 6 landcover classes - barren land, trees, grassland, roads, buildings and water bodies. 324,000 images were chosen as the training dataset and 81,000 were chosen as the testing dataset. The training and test sets were selected from disjoint NAIP tiles."@en ; dct:created "2015-11-03" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "Land cover" ; dct:creator "Louisiana State University", "NASA Ames Research Center", "Bay Area Environmental Research Institute", "NASA Advanced Supercomputing Division" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "TreeSatAI dataset"@en ; dct:description "The dataset was created for forest applications in Europe. It is highly suitable for benchmarking as it represents the real-world data situation of many German forest management services."@en ; dct:license ; dct:created "2022-08-01" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "Tree specie classification" ; dct:creator "Technische Universität Berlin, Geoinformation in Environmental Planning Lab", "Technische Universität Berlin, Remote Sensing Image Analysis Group", "Vision Impulse GmbH", "Deutsches Forschungszentrum für Künstliche Intelligenz, Smart Data and Knowledge Services" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "MADOS dataset"@en ; dct:description "Marine Debris and Oil Spill (MADOS) is a marine pollution dataset based on Sentinel-2 remote sensing data, focusing on marine litter and oil spills. MADOS formulates a challenging semantic segmentation task using sparse annotations."@en ; dct:license ; dct:created "2024-02-16" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Marine debris detection" ; dct:creator "National Technical University of Athens", "Hellenic Centre for Marine Research, Athens", "King Abdullah University of Science and Technology, Saudi Arabia" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "CLCD dataset"@en ; dct:description "The CLCD dataset consists of 600 pairs image of cropland change samples. Each group of samples is composed of two images and a corresponding binary label of cropland change. The main types of change annotated in CLCD include buildings, roads, lakes and bare soil lands, etc."@en ; dct:created "2022-05-23" ; ai4qc:taskType "change detection" ; dcat:keyword "change detection", "cropland change" ; dct:creator "Guangdong Provincial Key Laboratory for Urbanization and Geo-simulation, Sun Yat-sen University" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Massachusetts Buildings dataset"@en ; dct:description "The Massachusetts Buildings Dataset consists of 151 aerial images of the Boston area covering an area of 2.25 square kilometers. The target maps were obtained by rasterizing building footprints obtained from the OpenStreetMap project. The dataset covers mostly urban and suburban areas and buildings of all sizes, including individual houses and garages, are included in the labels."@en ; dct:created "2013-01-01" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Building detection" ; dct:creator "Graduate Department of Computer Science, University of Toronto" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "CropHarvest dataset"@en ; dct:description "The CropHarvest dataset is a crop dataset of geo-referenced labels with satellite data inputs, each consisting of latitude, longitude, the associated agricultural label, and a satellite pixel time series."@en ; dct:license ; dct:created "2023-12-04" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Crop type" ; dct:creator "University of Maryland", "NASA Harvest" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "GSFC_AI4QC dataset"@en ; dct:description "The dataset was used in the AI4QC project (Artificial Intelligence for Quality Control), in the context of cloud detection through semantic segmentation. It consists of a set of 27 reference cloud masks over NASA GSFC (coordinate reference system EPSG:32618) for various cloud conditions from September 2017 to November 2018. The cloud masks are associated to Sentinel-2 satellite data. The dataset is a smaller version of the GSFC dataset, where S2 L1C images were added, the cloud masks reclassifie"@en ; dct:license ; dct:created "2024-03-18" ; dct:modified "2024-03-18" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "cloud", "sentinel-2", "sky imaging" ; dct:creator "BVLabs" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "ISBDA dataset"@en ; dct:description "ISBDA stands for Instance Segmentation in Building Damage Assessment. The dataset contains user-generated aerial video frames from natural disasters annotated with instance-level building damage masks and bounding boxes. This dataset cointains 1030 images sampled from 10 aerial videos, which were recorded after Hurricane Harvey in 2017, Hurricane Micheal and Hurricane Florence in 2018 and other three tornadoes (EF-2 or EF-3) in 2017, 2018 and 2019."@en ; dct:license ; dct:created "2020-06-30" ; ai4qc:taskType "object detection" ; dcat:keyword "object detection", "building damage" ; dct:creator "Carnegie Mellon University" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "MultiScene dataset"@en ; dct:description "MultiScene is a large-scale dataset for multi-scene recognition in single aerial images. It contains 100000 high-resolution images and 36 scene categories."@en ; dct:created "2021-04-07" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "Multi-scene recognition" ; dct:creator "Remote Sensing Technology Institute, German Aerospace Center", "Data Science in Earth Observation, Technical University of Munich" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "GeoLifeCLEF 2021 dataset"@en ; dct:description "The dataset was created as part of a competition. It's motivation was to achieve automatic prediction of the list of species most likely to be observed at a given location."@en ; dct:license ; dct:created "2021-05-17" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Species prediction" ; dct:creator "CNRS", "LIRMM", "Univ Montpellier", "Department of Computing and Mathematical Sciences, Caltech" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "NaSC-TG2 dataset"@en ; dct:description "NaSC-TG2 stands for Natural Scene Classification With Tiangong-2 Remotely Sensed Imagery. It is a novel benchmark dataset for remote sensing scene classification from Tiangong-2 imagery. It contains 20 000 images, which are divided into ten scene classes, with 2000 images for each."@en ; dct:license ; dct:created "2021-03-02" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "Natural scene classification" ; dct:creator "University of Chinese Academy of Sciences", "Key Laboratory of Space Utilization", "Technology and Engineering Center for Space Utilization" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "SZTAKI AirChange dataset"@en ; dct:description "This Benchmark set contains 13 aerial image pairs and binary change masks. During the generation of the change mask, the following differences were considered as relevant changes: new built-up regions, building operations, planting of large group of trees, fresh plough-land and groundwork before building over."@en ; dct:license ; dct:created "2008-09-24" ; ai4qc:taskType "change detection" ; dcat:keyword "Change detection", "Optical aerial imagery" ; dct:creator "Computer and Automation Research Institute, Budapest", "Ariana Project-Team INRIA/CNRS/UNSA, France" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "LEVIR-CD dataset"@en ; dct:description "LEVIR-CD is a large-scale remote sensing binary change detection dataset, which would help develop novel deep learning-based algorithms for remote sensing image change detection."@en ; dct:license ; dct:created "2020-05-19" ; ai4qc:taskType "change detection" ; dcat:keyword "Change detection", "Urban change", "Very high-resolution" ; dct:creator "LEVIR Lab" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "HRPlanesv2 dataset"@en ; dct:description "HRPlanesv2 is a high-resolution satellite imagery dataset for aircraft detection. It contains 2120 very high-resolution Google Earth images, with a total of 14335 aircraft labeled."@en ; dct:license ; dct:created "2022-02-01" ; ai4qc:taskType "object detection" ; dcat:keyword "Object detection", "Aircraft detection" ; dct:creator "Dilsad Unsal" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Aerial Change Detection in Video Games (AICD) dataset"@en ; dct:description "The dataset consists of before and after scenes from Virtual Battle Station 2 with one major difference (new building, road, bush or car) and lots of smaller minor differences (lighting, weather, …). The goal was to automatically detect the major differences among the noisy background of other smaller differences."@en ; dct:created "2018-06-25" ; ai4qc:taskType "change detection" ; dcat:keyword "Video games imagery", "Change detection" ; dct:creator "EADS France", "CNRS" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "SIRI-WHU dataset"@en ; dct:description "The SIRI-WHU is a scene classification dataset comprised of 2400 images organized into 12 categories. Each class contains 200 images with a 2-m spatial resolution and a size of 200 x 200 pixels."@en ; dct:license ; dct:created "2015-11-17" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "Scene classification" ; dct:creator "State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University", "Collaborative Innovation Center of Geospatial Technology, The Chinese University of Hong Kong" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "AFO dataset"@en ; dct:description "AFO stands for Aerial dataset of Floating Objects. It is the first free dataset for training machine learning and deep learning models for maritime Search and Rescue applications. It contains aerial-drone videos with 40000 hand-annotated persons and objects floating in the water."@en ; dct:license ; dct:created "2021-06-01" ; ai4qc:taskType "object detection" ; dcat:keyword "Floating objects", "Object detection" ; dct:creator "Department of Electronics, AGH University of Science and Technology, Poland" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "SatlasPretrain for image classification dataset"@en ; dct:description "SatlasPretrain is a large-scale pre-training dataset for remote sensing image understanding. It consists of 302M labels under 137 categories and seven label types: points, polygons, polylines, properties, segmentation labels, regression labels, and classification labels. This description considers the image classification task."@en ; dct:license ; dct:created "2022-11-28" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "Image understanding" ; dct:creator "Allen Institute for AI" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "So2Sat dataset"@en ; dct:description "So2Sat LCZ42 is a dataset consisting of corresponding synthetic aperture radar and multispectral optical image data acquired by the Sentinel-1 and Sentinel-2 remote sensing satellites, and a corresponding local climate zones (LCZ) label. The dataset is distributed over 42 cities across different continents and cultural regions of the world, and comes with a split into fully independent, non-overlapping training, validation, and test sets."@en ; dct:license ; dct:created "2018-09-28" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "Climate zones" ; dct:creator "Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR)", "Signal Processing in Earth Observation (SiPEO), Technical University of Munich (TUM)" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Urban Drone Dataset (UDD-5)"@en ; dct:description "This is a collection of drone images collected at Peking University, Huludao city, Henan University and Cangzhou city. Urban Drone Dataset encompasses UDD-5 and UDD-6. UDD-5 contains 120 images and ground truths."@en ; dct:license ; dct:created "2019-11-04" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Drone images" ; dct:creator "Department of Computer Science and Technology, Peking University, China" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "TalloS dataset"@en ; dct:description "The dataset was created to improve the diversity and spatial coverage of FoMo-Bench. This dataset couples manual tree inventories with multispectral and SAR timeseries imagery for a fine grained multi-label classification challenge. It is based on the global Tallo database."@en ; dct:license ; dct:created "2025-03-01" ; ai4qc:taskType "multilabel classification" ; dcat:keyword "Multi-modal imagery", "Tree species classification" ; dct:creator "Harokopio University of Athens", "Mila - Quebec AI Institute", "Orion Lab, National Observatory of Athens & National Technical University of Athens", "McGill University" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "IDTReeS dataset"@en ; dct:description "The IDTReeS (Integrating Data science with Trees and Remote Sensing) dataset was created for a competition held in 2020. The aim was to delineate and classify individual tree crowns in NEON data. The data contains individual tree crown (ITC) spatial extent to be used for trainning. The classes are taxonomic species."@en ; dct:license ; dct:created "2020-07-07" ; ai4qc:taskType "object detection" ; dcat:keyword "Object detection", "Tree crown delineation", "Airborne imagery" ; dct:creator "WEECOLOGY, University of Florida" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "TenGeoP-SARwv dataset"@en ; dct:description "The TenGeoP-SARwv dataset contains images of Sentinel-1A wave mode (WV) in VV polarization. This dataset consists of more than 37000 SAR vignettes divided into ten defined geophysical categories, including both oceanic and meteorologic features. It was created to foster the development of approaches for ocean SAR image analysis."@en ; dct:license ; dct:created "2019-07-13" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "Sentinel-1", "Geophysical phenomena" ; dct:creator "Applied Physics Laboratory, University of Washington", "Ocean Processes Analysis Laboratory, University of New Hampshire", "Institut Mines-Télécom Atlantique, France", "Space and Ground Segment, France", "Laboratoire d'Océanographie Physique et Spatiale, France" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "ETCI 2021 Floods dataset"@en ; dct:description "This dataset contains data from the flood event detection contest, organized by the NASA Interagency Implementation and Advanced Concepts Team, which seeks to develop approaches to delineate open water flood areas as an effort to identify flood extent."@en ; dct:license ; dct:created "2021-04-15" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Flood", "Sentinel-1" ; dct:creator "NASA-IMPACT" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "EuroCropsML dataset"@en ; dct:description "EuroCropsML is a ready-to-use ML dataset combining EuroCrops reference data with Sentinel-2 reflectance data from 2021. It contains data from Latvia, Portugal, and Estonia and is intended for benchmarking few-shot crop type classification."@en ; dct:license ; dct:created "2024-09-04" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "Time series", "Crop type classification" ; dct:creator "Technical University of Munich", "ETH Zurich", "Zuse Institute, Berlin", "dida Datenschmiede GmbH, Berlin" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "CV4A Kenya Crop Type dataset"@en ; dct:description "This dataset was produced as part of the Crop Type Detection competition at the Computer Vision for Agriculture (CV4A) Workshop at the ICLR 2020 conference. The objective of the competition was to create a machine learning model to classify fields by crop type from images collected during the growing season by the Sentinel-2 satellites. The dataset is multitemporal."@en ; dct:license ; dct:created "2020-01-01" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Sentinel-2", "Crop type" ; dct:creator "Radiant Earth Foundation" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Find a Car Park dataset"@en ; dct:description "The dataset was created to identify if a free car space is available. The dataset contains raspberry pi images of a car park space used to identify and send a message to the user if a free space is available."@en ; dct:license ; dct:created "2019-05-27" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "Car park" ; dct:creator "David Carr" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "SpaceNet 4 dataset"@en ; dct:description "SpaceNet 4 was created for off-nadir building detection. The dataset was specifically built to explore advanced algorithms capabilities to process high off-nadir imagery. The dataset includes 27 WorldView-2 Satellite images from 7 degrees to 54 degrees off-nadir, all captured within 5 minutes of each other."@en ; dct:license ; dct:created "2018-10-19" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Building detection", "Off-nadir images" ; dct:creator "In-Q-Tel CosmiQ Works", "Radiant Solutions" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Crop Type Mapping (Ghana and South Sudan) dataset"@en ; dct:description "Re-release of the dataset from Rustowicz et al. in Ghana and South Sudan in a format more familiar to the machine learning community. The inputs are growing season time series of imagery from three satellites (Sentinel-1, Sentinel-2, and Planet’s PlanetScope) in 2016 and 2017, and the outputs are semantic segmentation of crop types."@en ; dct:license ; dct:created "2019-06-01" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Sentinel-2", "Crop type", "Sentinel-1", "PlanetScope" ; dct:creator "Sustainability and Artificial Intelligence Lab, Stanford University" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Rwanda Field Boundary dataset"@en ; dct:description "This dataset contains field boundaries for smallholder farms in eastern Rwanda. The dataset was created for the purpose of conducting the Rwanda Field boundary detection Challenge. The dataset includes rasterized labeled field boundaries and time series satellite imagery from Planet’s NICFI program. The paired dataset is provided in 256x256 chips for a total of 70 tiles covering 1532 individual fields."@en ; dct:license ; dct:created "2022-11-14" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Field boundary", "Planet imagery" ; dct:creator "Radiant Earth Foundation", "NASA Harvest", "TaQadam" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "SAR-Ship-Dataset dataset"@en ; dct:description "The dataset contains 102 images from the Gaofen-3 satellite and 108 images from Sentinel-1. The ships in the images were annotated with a bounding box. The data can be used to develop object detectors for multi-scale and small object detection."@en ; dct:created "2019-03-29" ; ai4qc:taskType "object detection" ; dcat:keyword "Object detection", "Ship detection" ; dct:creator "Key Laboratory of Digital Earth Science, Chinese Academy of Sciences" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "WHUMix dataset"@en ; dct:description "The WHU-Mix dataset is a diverse, large-scale, and high-quality dataset that aims to better simulate the situation of practical building extraction."@en ; dct:created "2023-04-18" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Building detection" ; dct:creator "School of Remote Sensing and Information Engineering, Wuhan University" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "GLH-Water dataset"@en ; dct:description "The dataset aims at global surface water detection in very-high-resolution (VHR) satellite imagery. It consists of 250 satellite images and 40.96 billion pixels labeled surface water annotations that are distributed globally and contain water bodies exhibiting a wide variety of types."@en ; dct:created "2023-03-15" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Water detection" ; dct:creator "Wuhan University, China" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "SpaceNet 3 dataset"@en ; dct:description "SpaceNet 3 is the third dataset released as part of the SpaceNet Challenge series. It focuses on road network detection from very high-resolution WorldView-3 satellite imagery. The dataset covers four cities (Las Vegas, Paris, Shanghai, Khartoum) with over 8000 kilometers of roads. SpaceNet 3 used the imagery from SpaceNet 2, only tiled into 400m chips."@en ; dct:license ; dct:created "2017-11-28" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Road Network Detection" ; dct:creator "In-Q-Tel CosmiQ Works", "Radiant Solutions" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "VisDrone-DET dataset"@en ; dct:description "VisDrone is a large-scale benchmark with carefully annotated ground-truth for various important computer vision tasks, to make vision meet drones. The dataset VisDrone-DET is designed for the object detection task and contains 10209 images."@en ; dct:created "2020-01-16" ; ai4qc:taskType "object detection" ; dcat:keyword "UAV imagery", "Object detection" ; dct:creator "Stony Brook University", "GE Global Research, Niskayuna", "University of North Texas", "University at Albany", "JD Finance America Corporation", "AISKYEYE team at Lab of Machine Learning and Data Mining, Tianjin University" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "CGI Planes dataset"@en ; dct:description "The dataset contains 500 computer generated satellite images of planes accompanied with an xml of the bounding box coordinates for each image."@en ; dct:created "2019-04-22" ; ai4qc:taskType "object detection" ; dcat:keyword "Object detection", "CGI plane detection" ; dct:creator "aceofspades914" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "AID-multilabel dataset"@en ; dct:description "AID multilabel dataset is produced from AID scene dataset. In total, there are 3000 aerial images (2400 training and 600 test) and 17 object labels. The size of each image is 600 x 600 pixels. In each set, images are sorted by their scene categories, defined in AID scene dataset."@en ; dct:created "2020-02-07" ; ai4qc:taskType "multilabel classification" ; dcat:keyword "Land cover", "Multilabel classification" ; dct:creator "Remote Sensing Technology Institute, German Aerospace Center" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "BDCI2020 dataset"@en ; dct:description "The dataset was created for the Big Data and Computational Intelligence Competition (BDCI), founded by the China Computer Society in 2013. It contains 145981 images and masks, with 7 annotation classes."@en ; dct:license ; dct:created "2020-10-18" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation" ; dct:creator "Baidu Brain AI Studio" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "DFC23 dataset"@en ; dct:description "DFC23 establishes a large-scale, fine-grained, and multi-modal benchmark for the classification of building roof types. It consists of two challenging competition tracks investigating the fusion of optical and SAR data, while focusing on roof type classification and building height estimation, respectively."@en ; dct:license ; dct:created "2023-07-03" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Building detection" ; dct:creator "Aerospace Information Research Institute, China", "GEOVIS Earth Technology Co.", "University of Twente, The Netherlands", "DLR", "Universität der Bundeswehr München", "National Research Council, Italy" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "WHDLD-multilabel dataset"@en ; dct:description "WHDLD (Wuhan Dense Labeling Dataset) is a dense labeling dataset that can be used for multi-label tasks such as remote sensing image retrieval (RSIR) and classification, and other pixel-based tasks like semantic segmentation. This subset considers the multi-label classification task."@en ; dct:created "2018-06-14" ; ai4qc:taskType "multilabel classification" ; dcat:keyword "Multilabel classification", "Dense labeling dataset" ; dct:creator "State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University", "Nanjing University of Information Science and Technology" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "m-pv4ger dataset"@en ; dct:description "M-pv4ger is a modified version of the pv4ger dataset. This original dataset was modified in order to be compatible with Geo-Bench. It can be used for image classification tasks to detect rooftop-mounted solar photovoltaic systems."@en ; dct:license ; dct:created "2023-06-06" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "Solar photovoltaic detection" ; dct:creator "Technical University of Munich", "ETH Zurich", "University of Maryland", "Stanford University", "Massachusetts Institute of Technology", "University of Amsterdam", "ServiceNow Research", "University of Montreal", "Clark University" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "UAVid dataset"@en ; dct:description "The UAVid dataset was designed to enhance the semantic understanding of urban scenes. The data acquisition and annotation methodology is designed for UAV semantic segmentation in complex urban scenes, featuring on both static and moving object recognition."@en ; dct:license ; dct:created "2020-05-30" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Urban scene" ; dct:creator "State Key Lab. of LIESMARS, Wuhan University", "Department of Civil, Environmental and Geodetic Engineering, Ohio State University", "Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "CNAM-CD dataset"@en ; dct:description "CNAM-CD is a pixel-level multi-category change detection dataset from different Chinese national new areas. In this dataset A is the image at the previous time, B is the image at the later time and Label is the corresponding label."@en ; dct:license ; dct:created "2023-07-24" ; ai4qc:taskType "change detection" ; dcat:keyword "change detection", "land cover" ; dct:creator "Xinjiang University" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "HLS Burn Scars dataset"@en ; dct:description "This dataset contains Harmonized Landsat and Sentinel-2 imagery of burn scars and the associated masks for the years 2018-2021 over the contiguous United States. There are 804 512x512 scenes. Its primary purpose is for training geospatial machine learning models."@en ; dct:license ; dct:created "2023-06-29" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Burn scars detection" ; dct:creator "The University of Alabama in Huntsville" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "xView Dataset"@en ; dct:description "The DIUx xView: Objects in Context in Overhead Imagery is a large-scale dataset designed to advance object detection techniques and research in overhead object detection. The dataset was collected using WorldView-3 satellites, providing high-resolution imagery."@en ; dct:license ; dct:created "2018-03-30" ; ai4qc:taskType "object detection" ; dcat:keyword "Object detection", "High-resolution imagery" ; dct:creator "DigitalGlobe", "Defense Innovation Unit Experimental (DIUx)", "National Geospatial-Intelligence Agency (NGA)" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "RarePlanes dataset"@en ; dct:description "RarePlanes is a unique computer vision dataset combining synthetic and real satellite imagery. It consists of 253 Maxar WorldView-3 satellite scenes, paired with 50000 synthetic satellite images generated using AI.Reverie's simulation platform."@en ; dct:license ; dct:created "2020-06-04" ; ai4qc:taskType "object detection" ; dcat:keyword "Object detection", "Plane recognition", "Synthetic imagery" ; dct:creator "AI.Reverie", "IQT - CosmiQ Works" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "TTPLA dataset"@en ; dct:description "TTPLA is an aerial-image dataset for detection and segmentation of transmission towers (TTs) and power lines (PLs). The dataset consists of 1100 images, as well as manually labeled 8987 instances of TTs and PLs."@en ; dct:license ; dct:created "2020-10-19" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Transmission towers", "Power lines", "Instance segmentation" ; dct:creator "University of South Carolina, USA" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "ChaBuD dataset"@en ; dct:description "ChaBuD is a dataset for Change detection for Burned area Delineation and is used for the ChaBuD ECML-PKDD 2023 Discovery Challenge."@en ; dct:created "2023-12-06" ; ai4qc:taskType "change detection" ; dcat:keyword "Sentinel-2", "Change detection", "Burned area" ; dct:creator "ETH Zurich", "University of Zurich", "Agroscope, Switzerland", "Federal Office for Agriculture, Switzerland" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Satellite Image Classification dataset"@en ; dct:description "The dataset is created for remote sensing image interpretation. It consists of 5631 images in four classes."@en ; dct:created "2021-08-21" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "General scenes" ; dct:creator "Mahmoud Reda" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "WiDS Datathon 2019 dataset"@en ; dct:description "The Global WiDS team, the West Big Data Innovation Hub, and the WiDS Datathon Committee have partnered with Planet and Figure Eight to bring more than 20,000 labeled satellite images to participants. The WiDS Datathon challenge is to create a model that predicts the presence of oil palm plantations in satellite imagery."@en ; dct:license ; dct:created "2019-02-28" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "Oil palm plantation" ; dct:creator "WiDS Datathon" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Chactun dataset"@en ; dct:description "This dataset consists of tiles derived from Sentinel-1, Sentinel-2, and ALS (lidar) data, and associated annotation masks. The main study area is a region around Chactún, Mexico. The dataset can be used for building ML models for prediction of the segmentation masks for each tile. In particular, for each tile, one has three masks, one for each of the classes of man-made structures - buildings, platforms, and aguadas."@en ; dct:created "2020-07-10" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Archeology" ; dct:creator "Jožef Stefan Institute, Slovenia", "Jožef Stefan International Postgraduate School, Slovenia", "Research Centre of the Slovenian Academy of Sciences and Arts (ZRC SAZU)" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Iceberg Classifier Challenge dataset"@en ; dct:description "The Statoil/C-CORE Iceberg Classifier Challenge dataset contains Sentinel-1 SAR images for distinguishing between ships and icebergs in Arctic waters. Each sample includes dual-polarization (HH and HV) radar data, with labels indicating whether the object is an iceberg or a ship."@en ; dct:license ; dct:created "2017-10-23" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "Sentinel-1", "iceberg detection" ; dct:creator "Statoil", "C-CORE" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Amazon Rainforest dataset"@en ; dct:description "Amazon Rainforest dataset contains images used for the semantic segmentation of forest and non-forest areas from a fully convolutional neural network U-Net."@en ; dct:license ; dct:created "2019-05-27" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Forest segmentation" ; dct:creator "Universidade Federal da Fronteira Sul" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Austin Zoning Satellite Images dataset"@en ; dct:description "The dataset was created to replicate the project shown in the City Class project by Roman Kuchokov, on a different dataset. The objective is to classify a satellite picture of a small part of a city into one of its zones (such as residential, industrial, etc). It contains 3666 small satellite images of the whole city of Austin, each being labeled with the corresponding zoning tag."@en ; dct:license ; dct:created "2018-01-07" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "Zoning tag" ; dct:creator "Daniel Franch" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "SAR-ACD dataset"@en ; dct:description "SAR-ACD is a SAR aircraft category dataset with higher scene complexity. It was created to improve the field of SAR target classification. It contains 6 civil aircraft categories."@en ; dct:created "2023-03-14" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "SAR imagery", "Aircraft detection" ; dct:creator "Aerospace Information Research Institute, Chinese Academy of Sciences", "Key Laboratory of Network Information System Technology, Chinese Academy of Sciences", "School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "UBC dataset"@en ; dct:description "The Urban Building Classification (UBC) dataset was created for building detection and classification from very high-resolution satellite imagery with the focus on object-level interpretation of individual buildings. It is meant to provide not only a flexible test platform for object detection algorithms but also a solid basis for the comparison of city morphologies and the investigation of urban planning."@en ; dct:license ; dct:created "2022-06-19" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Building detection" ; dct:creator "Aerospace Information Research Institute, Chinese Academy of Sciences", "University of Chinese Academy of Sciences", "German Aerospace Center", "Universitat der Bundeswehr Munchen, Germany", "Technische Universitat Munchen, Germany" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "UCAS_AOD dataset"@en ; dct:description "The dataset is a remote sensing image dataset that contains two types of targets: cars and airplanes, as well as background negative samples. This dataset was first released by the University of Science and Technology of China in 2014 and supplemented in 2015."@en ; dct:created "2015-12-10" ; ai4qc:taskType "object detection" ; dcat:keyword "Object detection", "Aerial RGB" ; dct:creator "University of Science and Technology of China" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Functional Map of the World (fMoW) dataset"@en ; dct:description "The dataset aims to inspire the development of machine learning models capable of predicting the functional purpose of buildings and land use from temporal sequences of satellite images and a rich set of metadata features."@en ; dct:license ; dct:created "2017-11-21" ; ai4qc:taskType "object detection" ; dcat:keyword "Object detection", "DigitalGlobe constellation", "Functional map" ; dct:creator "DigitalGlobe", "The Johns Hopkins University Applied Physics Laboratory" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "RESISC45 dataset"@en ; dct:description "RESISC45 dataset is a publicly available benchmark for Remote Sensing Image Scene Classification (RESISC), created by Northwestern Polytechnical University (NWPU). This dataset contains 31,500 images, covering 45 scene classes with 700 images in each class."@en ; dct:created "2017-04-03" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "Scene classification" ; dct:creator "School of Automation, Northwestern Polytechnical University, China", "Center for OPTical IMagery Analysis and Learning (OPTIMAL), Chinese Academy of Sciences" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Season-varying Change Detection dataset"@en ; dct:description "As a common dataset for change detection, the dataset contains three parts: the image before the change, the image after the change, and the label image showing the changed area. This dataset is characterized by significant seasonal differences between bi-temporal image pairs. The labels for this dataset include some irregular changes, such as the appearance and disappearance of cars; but do not include seasonal changes, such as changes in the ground surface caused by snowfall."@en ; dct:created "2018-06-04" ; ai4qc:taskType "change detection" ; dcat:keyword "High-resolution imagery", "Seasonal change detection" ; dct:creator "State Res. Institute of Aviation Systems (GosNIIAS)" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "CESBIO_AI4QC dataset"@en ; dct:description "This dataset was used in the AI4QC project (Artificial Intelligence for Quality Control), in the context of cloud detection through semantic segmentation. It consists of a set of reference cloud masks generated by an active learning method for 30 Sentinel-2 scenes over 10 sites: Alta Floresta (Brazil), Arles (France), Ispra (Italy), Munich (Germany), Orleans (France), Gobabeb (Namibia), Marrakech (Morocco), Railroad Valley (USA), Pretoria (South AFrica) and Mongu (Zambia). The cloud masks are gl"@en ; dct:license ; dct:created "2024-03-18" ; dct:modified "2024-03-18" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "cloud mask", "sentinel-2" ; dct:creator "BVLabs" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "MAR20 dataset"@en ; dct:description "MAR20 dataset is a publicly available remote sensing image Military Aircraft Recognition dataset used for research purposes only. This dataset includes 3842 images, 20 types, and 22341 instances annotated with horizontal bounding boxes and oriented bounding boxes."@en ; dct:license ; dct:created "2023-12-07" ; ai4qc:taskType "object detection" ; dcat:keyword "Object detection", "Military aircraft" ; dct:creator "School of Automation, Northwestern Polytechnical University, China" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Sentinel-2 Cloud Mask Catalogue dataset"@en ; dct:description "This dataset comprises cloud masks for 513 subscenes, at 20m resolution, sampled randomly from the 2018 Level-1C Sentinel-2 archive."@en ; dct:license ; dct:created "2020-11-09" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "semantic segmentation", "cloud detection", "meteorology" ; dct:creator "World Food Programme", "University College London" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "RSI-CB128 dataset"@en ; dct:description "RSI-CB stands for Large Scale Remote Sensing Image Classification Benchmark via Crowdsource Data. RSI-CB128 containss more than 36000 images of size 128 x 128 pixel, and 45 categories."@en ; dct:created "2017-05-30" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "Crowdsource data" ; dct:creator "School of Geosciences and Info-Physics, Central South University" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "UC Merced dataset"@en ; dct:description "UC Merced is a 21 class land use remote sensing image dataset, with 100 images per class. The images were manually extracted from large images from the USGS National Map Urban Area Imagery collection for various urban areas around the country. The pixel resolution of this public domain imagery is 0.3 m."@en ; dct:license ; dct:created "2010-11-02" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "Land use" ; dct:creator "Electrical Engineering & Computer Science, University of California at Merced" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "CESBIO dataset"@en ; dct:description "Set of reference cloud mask data set generated by an active learning method for 38 Sentinel-2 scenes over 10 sites: Alta Floresta (Brazil), Arles (France), Ispra (Italy), Munich (Germany), Orleans (France), Gobabeb (Namibia), Marrakech (Morocco), Railroad Valley (USA), Pretoria (South AFrica) and Mongu (Zambia). The scenes are collected for different seasons and cloud cover types."@en ; dct:license ; dct:created "2018-10-12" ; dct:modified "2020-01-24" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "cloud mask", "sentinel-2" ; dct:creator "ESA", "CESBIO", "CNES" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "DLRSD dataset"@en ; dct:description "DLRSD (Dense Labeling Remote Sensing Dataset) is a dense labeling dataset that can be used for multi-label tasks such as remote sensing image retrieval (RSIR) and classification, and other pixel-based tasks like semantic segmentation. It has 21 broad categories with 100 images per class, and 17 segmentation classes."@en ; dct:created "2018-06-14" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Dense labeling dataset" ; dct:creator "State Key Laboratory of Information Engineering in Surveying, Wuhan University" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "SpaceNet 2 dataset"@en ; dct:description "SpaceNet 2 is the second dataset released as part of the SpaceNet Challenge series. It focuses on building footprint detection from very high-resolution WorldView-3 satellite imagery. The dataset covers four cities (Las Vegas, Paris, Shanghai, Khartoum) with over 300000 building annotations. Images are provided at ~0.3 m resolution, and the corresponding labels are vector building footprints."@en ; dct:license ; dct:created "2017-05-01" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Building detection" ; dct:creator "In-Q-Tel CosmiQ Works", "Radiant Solutions" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "AiRound dataset"@en ; dct:description "AiRound is composed of 11753 images distributed among 11 classes. Each sample is composed by a triplet, that contains images in 3 distinct points of view: a ground perspective image; a high resolution RGB aerial image; and a multi-spectral image taken from the Sentinel-2 satellite."@en ; dct:created "2020-07-22" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "Multi-source inagery" ; dct:creator "Department of Computer Science, Universidade Federal de Minas Gerais, Brazil" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "DLR-SkyScapes dataset"@en ; dct:description "SkyScapes is an aerial image dataset with highly-accurate, fine-grained annotations for pixel-level semantic labeling. It was created to capture the complexity of aerial scenes at the level of granularity required by real-world applications."@en ; dct:license ; dct:created "2020-07-12" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Urban semantic segmentation" ; dct:creator "German Aerospace Center (DLR)" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Airbus Ship Detection Dataset"@en ; dct:description "The Airbus Ship Detection Challenge dataset consists of high-resolution satellite imagery with annotated ship locations. Released as part of a Kaggle competition, it contains over 190000 satellite images captured by Airbus from Planet and DigitalGlobe satellites. The dataset includes a CSV file that provides pixel coordinates for each image, representing ships' segmentation boxes."@en ; dct:license ; dct:created "2018-07-30" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic Segmentation", "Ship detection" ; dct:creator "Airbus" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "SIMD dataset"@en ; dct:description "The Satellite Imagery Multi-vehicles Dataset (SIMD) contains 5000 images with 45096 objects in 15 classes."@en ; dct:created "2021-01-27" ; ai4qc:taskType "object detection" ; dcat:keyword "Object detection", "Vehicle detection" ; dct:creator "School of Eletrical Engineering & Computer Science, National University of Sciences & Technology, Pakistan" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Agriculture-Vision dataset"@en ; dct:description "The dataset was created for the CVPR 2020 challenge. It contains 21061 aerial farmland images. Each image also has a boundary map and a mask. The boundary map indicates the region of the farmland, and the mask indicates valid pixels in the image."@en ; dct:license ; dct:created "2020-04-25" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Agriculture" ; dct:creator "Intelinair", "UIUC", "University of Oregon" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "VEDAI dataset"@en ; dct:description "VEDAI is a dataset for Vehicle Detection in Aerial Imagery, provided as a tool to benchmark automatic target recognition algorithms in unconstrained environments. The vehicles contained in the database, in addition of being small, exhibit different variabilities such as multiple orientations, lighting/shadowing changes, specularities or occlusions."@en ; dct:created "2015-12-16" ; ai4qc:taskType "object detection" ; dcat:keyword "Object detection", "Vehicle recognition" ; dct:creator "Safran", "University of Caen" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "DSIFN dataset"@en ; dct:description "The DSIFN (Deeply Supervised Image Fusion Network) dataset consists of six large bi-temporal high resolution images clipped into 3988 patches. It is used for change detection."@en ; dct:license ; dct:created "2020-09-12" ; ai4qc:taskType "change detection" ; dcat:keyword "Change detection" ; dct:creator "Wuhan University", "Collaborative Innovation Center of Geospatial Technology", "Italian Space Agency (ASI)" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "ForestNet dataset"@en ; dct:description "The dataset was developed to automate forest loss driver classification and aid the development and implementation of targeted forest conservation and management policies. The dataset consists of 2,756 satellite images of forest loss events with driver annotations."@en ; dct:license ; dct:created "2020-11-11" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Landsat 8", "deforestation" ; dct:creator "Stanford University", "RTI International", "Descartes Labs" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Bijie Landslide dataset"@en ; dct:description "The Bijie landslide dataset is a remote sensing landslide dataset for developing automatic landslide detection methods. The dataset consists of satellite optical images, shapefiles of landslides' boundaries and digital elevation models. All the images in this dataset, i.e. 770 landslide images and 2003 non-landslide images were cropped from the TripleSat satellite images captured from May to August 2018."@en ; dct:created "2020-02-01" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "Landslide detection" ; dct:creator "Beijing Forestry University", "State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology", "School of Remote Sensing and Information Engineering, Wuhan University", "Third Surveying and Mapping Institute of Guizhou Province" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "SpaceNet 5 dataset"@en ; dct:description "SpaceNet 5 focuses on the task of road network extraction in challenging geographic regions. The dataset provides satellite imagery (30 cm resolution) across multiple cities, along with labeled road networks in vector format."@en ; dct:license ; dct:created "2019-09-03" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Road network detecton", "Speed prediction" ; dct:creator "In-Q-Tel CosmiQ Works" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "SSL4EO-L Benchmark dataset"@en ; dct:description "The dataset is intended to be used for evaluation of SSL techniques. It consists of 25,000 images with corresponding land cover classification masks."@en ; dct:license ; dct:created "2023-06-15" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Land cover", "Landsat" ; dct:creator "Technical University of Munich", "German Aerospace Center", "Microsoft AI for Good Research Lab", "University of Illinois Urbana-Champaign", "University of Texas at San Antonio" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "MASATI for object detection dataset"@en ; dct:description "The MASATI (MAritime SATellite Imagery) dataset provides maritime scenes of optical aerial images. It can be used to evaluate ship detection methods. It contains 7389 satellite images and can be used for image classification, or object detection for the 'coast_ship', 'ship' and 'multi' classes. This description considers the object detection task."@en ; dct:created "2019-06-01" ; ai4qc:taskType "object detection" ; dcat:keyword "Object detection", "Maritime images" ; dct:creator "Automation, Robotics and Computer Vision Group, University of Alicante", "Pattern Recognition and Artificial Intelligence Group, University of Alicante" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "ArtifiVe-Potsdam dataset"@en ; dct:description "ArtifiVe-Potsdam (Artificial Vehicles) is a benchmark dataset for vehicle detection in aerial imagery. It combines real data from the Potsdam dataset with artificially generated vehicle images to study the impact of artificial data enrichment on machine learning performance."@en ; dct:license ; dct:created "2021-10-12" ; ai4qc:taskType "object detection" ; dcat:keyword "object detection", "artificial imagery", "vehicle detection" ; dct:creator "University of Bonn, Institute of Geodesy and Geoinformation", "University of Applied Sciences Koblenz" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "S2UV dataset"@en ; dct:description "S2UV stands for Satellite & Street-view images for Urban Village classification. The dataset contains 856 and 1714 image samples corresponding to the two categories of urban villages and non-urban villages, respectively, which are sampled in Beijing, Tianjin and Shijiazhuang."@en ; dct:license ; dct:created "2021-12-30" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "Google Earth", "Urban village", "Street-view images" ; dct:creator "Chinese Academy of Sciences", "China Agricultural University", "Shandong Jianzhu University" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "HistAerial dataset"@en ; dct:description "HistAerial is a large scale dataset for historical aerial images classification. It contains more than 4.9 million samples. The images span a 20 years period and 3 different scales."@en ; dct:created "2019-01-31" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "Historical images" ; dct:creator "Leon Berard Center, France", "Univ Lyon, France", "French Environment and Energy Management Agency" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "SpaceNet 8 dataset"@en ; dct:description "SpaceNet 8 is a multi-task remote sensing dataset combining building footprint detection, road network extraction, and flood attribution using pre- and post-event imagery covering three AOIs."@en ; dct:license ; dct:created "2022-07-12" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Flood detection" ; dct:creator "Oak Ridge National Laboratory", "German Aerospace Center (DLR)", "Maxar Technologies" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "DeepGlobe (road detection) dataset"@en ; dct:description "The dataset was created for the DeepGlobe Road Extraction Challenge, which poses the challenge of automatically extracting roads and street networks from satellite images."@en ; dct:license ; dct:created "2018-05-17" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Road extraction", "DigitalGlobe constellation" ; dct:creator "DigitalGlobe", "The MIT Media Lab", "Wageningen University", "Facebook", "CosmiQ Works" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Zi-Yuan 3 dataset"@en ; dct:description "The Zi-Yuan 3 (ZY3) data set is from the region of Wuhan, China. It was designed for the change detection task."@en ; dct:license ; dct:created "2020-04-01" ; ai4qc:taskType "change detection" ; dcat:keyword "Change detection", "Zi Yuan-3" ; dct:creator "School of Remote Sensing and Information Engineering, Wuhan University" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "MASATI for image classification dataset"@en ; dct:description "The MASATI (MAritime SATellite Imagery) dataset provides maritime scenes of optical aerial images. It can be used to evaluate ship detection methods. It contains 7389 satellite images and can be used for image classification, or object detection for the 'coast_ship', 'ship' and 'multi' classes. This description considers the image classification task."@en ; dct:created "2019-06-01" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "Maritime images" ; dct:creator "Automation, Robotics and Computer Vision Group, University of Alicante", "Pattern Recognition and Artificial Intelligence Group, University of Alicante" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Hollstein_AI4QC dataset"@en ; dct:description "This dataset was used for the AI4QC project (Artificial Intelligence for Quality Control), in the context of cloud detection through semantic segmentation. It consists of a database of 58 manually labeled Sentinel-2 spectra of clouds. The scenes are globally distributed and were acquired in 2016. The dataset originates from the Hollstein dataset, where the cloud masks were reclassified and converted into raster TIFF format. The new classes are the following: 0-clear, 1-thick cloud, 2-thin cloud,"@en ; dct:license ; dct:created "2024-03-20" ; dct:modified "2024-03-20" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "cloud detection", "sentinel-2", "machine learning" ; dct:creator "BVLabs" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "SECOND dataset"@en ; dct:description "SEmantic Change detectiON Dataset (SECOND) is a well-annotated semantic change detection dataset. It contains 4662 pairs of aerial images from several platforms and sensors. Each image is annotated at the pixel level. The change category in the SECOND dataset focuses on 6 main land-cover classes, i.e., non-vegetated ground surface, tree, low vegetation, water, buildings and playgrounds, that are frequently involved in natural and man-made geographical changes. These 6 selected land-cover categor"@en ; dct:created "2020-10-12" ; ai4qc:taskType "change detection" ; dcat:keyword "Change detection", "Land cover" ; dct:creator "Wuhan University", "DAIS, University of Venice", "State Key Lab. of LIESMARS" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "RSI-CB256 dataset"@en ; dct:description "RSI-CB stands for Large Scale Remote Sensing Image Classification Benchmark via Crowdsource Data. RSI-CB256 containss more than 24000 images of size 256 x 256 pixel, and 35 categories."@en ; dct:created "2017-05-30" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "Crowdsource data" ; dct:creator "School of Geosciences and Info-Physics, Central South University" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "LEVIR-CD+ dataset"@en ; dct:description "LEVIR-CD+ is an extended version of the original LEVIR-CD dataset, designed for building change detection tasks in urban areas using remote sensing imagery."@en ; dct:license ; dct:created "2021-12-27" ; ai4qc:taskType "change detection" ; dcat:keyword "Change detection", "Urban change", "Very high-resolution" ; dct:creator "LEVIR Lab" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "SeaDronesSee dataset"@en ; dct:description "SeaDronesSee is a large-scale dataset aimed at helping develop systems for Search and Rescue (SAR) using Unmanned Aerial Vehicles (UAVs) in maritime scenarios. This dataset provides three sets of tracks: object detection, single-object tracking and multi-object tracking. Each track consists of its own fully labeled dataset."@en ; dct:created "2021-12-15" ; ai4qc:taskType "object detection" ; dcat:keyword "Object detection", "Open water search" ; dct:creator "University of Tuebingen" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "DFC21-DSE dataset"@en ; dct:description "The DFC21-DSE dataset was created for the detection of settlements without electricity challenge track (Track DSE) of the 2021 IEEE GRSS Data Fusion Contest. It contains multitemporal and multimodal data."@en ; dct:license ; dct:created "2021-05-03" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Settlements classification" ; dct:creator "AI Strategy and Solutions, Hewlett Packard Enterprise Co.", "German Aerospace Center", "Helmholtz-Zentrum Dresden-Rossendorf", "University of Tokyo", "GIPSA-Lab" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "California Flood dataset"@en ; dct:description "The dataset consists of three images (time1, time2, ground truth) which are resampled, co-registered and cropped to cover an area affected by a flood."@en ; dct:license ; dct:created "2019-08-14" ; ai4qc:taskType "change detection" ; dcat:keyword "Change detection", "Flood event" ; dct:creator "The Arctic University of Norway" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "BH-Watertanks dataset"@en ; dct:description "BH-Watertanks contains annotated water tanks over Belo Horizonte in Brazil. The data was acquired through the Google Earth Pro tool. The images were exported from an eye altitude of 330 meters. The dataset consists of 150 images of 8 different neighbourhoods and contains 13216 annotated water tanks."@en ; dct:created "2020-07-29" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Google Earth", "Watertank detection" ; dct:creator "Department of Computer Science, Universidade Federal de Minas Gerais, Brazil" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "GSFC dataset"@en ; dct:description "Reference cloud data over NASA GSFC for various cloud conditions from September 2017 to November 2018. The reference dataset is used for validating cloud masking algorithms for moderate spatial resolution satellite data (10-30m), namely Sentinel-2 and Landsat 8."@en ; dct:license ; dct:created "2020-11-06" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "cloud", "sentinel-2", "validation", "sky imaging" ; dct:creator "University of Maryland", "NASA Goddard Space Fligth Center" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "ITCVD dataset"@en ; dct:description "ITCVD dataset is a large-scale, well annotated (all vehicles in a scene are annotated) and challenging vehicle detection dataset. The images were taken from an airplane platform at 330m height over Enschede in The Netherlands."@en ; dct:license ; dct:created "2018-01-22" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Object detection", "Vehicle detection" ; dct:creator "Institute of Information Processing, Leibniz University Hannover", "ITC Faculty of Geo-Information Science and Earth Observation, University of Twente" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "MTS12 dataset"@en ; dct:description "The Multi-Temporal Sentinel-1/2 (MTS12) Dataset is composed of optical Sentinel-2 and SAR Sentinel-1 time series covering the whole Slovenia, an area of 20,271 square kilometers, for land cover classification. The whole dataset included 936 patches, each with the size of 500x500 pixels and the temporal dimension of 12 (one per month). The labels contain 8 land-cover classes."@en ; dct:license ; dct:created "2022-11-07" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Sentinel-2", "Sentinel-1" ; dct:creator "School of Remote Sensing and Information Engineering, Wuhan University" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Hollstein dataset"@en ; dct:description "The S2 Hollstein dataset is a globally distributed database of manually labeled Sentinel-2 spectra of clouds."@en ; dct:license ; dct:created "2016-08-18" ; dct:modified "2017-07-10" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "cloud detection", "sentinel-2", "machine learning" ; dct:creator "ESA", "GFZ German Research Centre for Geosciences" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Cashew Plantation dataset"@en ; dct:description "This dataset contains labels for cashew plantations in a 120 km^2 area in the center of Benin. Each pixel is classified for Well-managed plantation, Poorly-managed plantation, No plantation and other classes."@en ; dct:license ; dct:created "2023-09-01" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Cashew plantation" ; dct:creator "McKinsey & Company", "TechnoServe Labs", "PULA, Nairobi", "University of Minnesota" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "FAIR1M dataset"@en ; dct:description "FAIR1M is a benchmark dataset for fine-grained object recognition in high resolution remote sensing imagery. It contains more than 15000 images and 1 milion instances. Instances are annotated with respect to 5 categories and 37 sub-categories using oriented bounding boxes."@en ; dct:created "2021-04-28" ; ai4qc:taskType "object detection" ; dcat:keyword "Object detection", "High resolution imagery" ; dct:creator "Chinese Academy of Sciences", "Karlsruhe Institute of Technology", "Beijing Remote Sensing Information Institute", "Fujian Collaborative Innovation Center for Big Data Applications in Governments", "Fudan University", "Xiamen University" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Hyperspectral Change Detection dataset"@en ; dct:description "This dataset can be used to perform change detection techniques in multitemporal hyperspectral images. It includes two different hyperspectral scenes from the AVIRIS sensor: Santa Barbara scene, whose spatial dimensions are 984 x 740 pixels, spatial resolution 15.2 meters per pixel and includes 224 spectral bands. Bay Area scene, whose spatial dimensions are 600 x 500 pixels, spatial resolution 16.9 meters per pixel and includes 224 spectral bands. It also includes a hyperspectral scene from the"@en ; dct:license ; dct:created "2017-12-16" ; ai4qc:taskType "change detection" ; dcat:keyword "Change detection", "Hyperspectral imagery" ; dct:creator "Centro Singular de Investigación en Tecnoloxías da Información (CiTIUS)" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "KappaSet_AI4QC dataset"@en ; dct:description "The dataset was used in the AI4QC project (Artificial Intelligence for Quality Control), in the context of semantic segmentation for cloud detection. It consists of 9251 cloud masks, distributed over the full globe and acquired over the four seasons of 2020. To each label file is associated a Sentinel-2 L1C image. The dataset originates from the KappaSet dataset, where the cloud masks were reclassified. The new classes are the following: 0-clear, 1-thick cloud, 2-thin cloud, 3-cloud shadow. The "@en ; dct:license ; dct:created "2024-04-15" ; dct:modified "2024-04-15" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "cloud mask", "semantic segmentation", "sentinel-2", "cloud shadow" ; dct:creator "BVLabs" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "SA Crop Type dataset"@en ; dct:description "This dataset was produced as part of the Radiant Earth Spot the Crop Challenge (https://zindi.africa/hackathons/radiant-earth-spot-the-crop-hackathon). The objective of the competition was to create a machine learning model to classify fields by crop type from images collected during the growing season by the Sentinel-2 and Sentinel-1 satellites."@en ; dct:license ; dct:created "2021-07-05" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Sentinel-2", "Crop type", "Sentinel-1" ; dct:creator "Radiant Earth Foundation" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "OpenSentinelMap dataset"@en ; dct:description "The OpenSentinelMap dataset contains Sentinel-2 imagery and per-pixel semantic label masks derived from OpenStreetMap."@en ; dct:license ; dct:created "2022-04-19" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Sentinel-2", "Land use" ; dct:creator "Vision Systems Inc" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "ASD dataset"@en ; dct:description "This dataset is provided for research on aerial image segmentation. The dataset contains 80 high-resolution aerial images. To each image is associated a segmentation mask where objects were delineated."@en ; dct:created "2013-06-27" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Aerial imagery" ; dct:creator "Oak Ridge National Laboratory" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "xView2 dataset"@en ; dct:description "The xView2 dataset is designed for automated building damage assessment in the aftermath of natural disasters using satellite imagery. It was created as part of the xView2 Challenge organized by DARPA and the DIUx xView Team. The dataset contains pre- and post-disaster imagery along with detailed annotations marking buildings and classifying them into damage levels: no damage, minor, major, and destroyed."@en ; dct:license ; dct:created "2019-11-21" ; ai4qc:taskType "change detection" ; dcat:keyword "Change detection", "Disaster response" ; dct:creator "Carnegie Mellon University", "Department of Defense", "Software Engineering Institute", "Defense Innovation Unit", "CrowdAI, Inc." ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "SpaceNet 7 dataset"@en ; dct:description "SpaceNet 7 is a large-scale dataset for multi-temporal analysis of urban development. It consists of 24 consecutive monthly mosaic images, comprising more than 40000 km2 of imagery and over 500000 unique building footprint labels. The satellite imagery was collected by Planet Labs' Dove Satellites. It enables research on building detection, urban change detection, and foundation mapping in rapidly evolving areas."@en ; dct:license ; dct:created "2020-12-01" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Time series", "Building detection" ; dct:creator "In-Q-Tel CosmiQ Works" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "AeroRIT dataset"@en ; dct:description "AeroRIT: A New Scene for Hyperspectral Image Analysis, is a scene overlooking Rochester Institute of Technology captured via Headwall Photonics Micro Hy-perspec E-Series CMOS sensor at an altitude of approximately 5000 feat. The semantic map pixels were labelled with ENVI 4.8.2, using individual hyperspectral signatures and the geo-registered RGB images as references."@en ; dct:license ; dct:created "2020-05-30" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Hyperspectral" ; dct:creator "ON Semiconductor", "Rochester Institute of Technology" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "GID-15 dataset"@en ; dct:description "GID-15 is a large-scale land-cover dataset with Gaofen-2 (GF-2) satellite images. This dataset, which is named as Gaofen Image Dataset with 15 categories (GID-15), has superiorities over the existing land-cover dataset because of its large coverage, wide distribution, and high spatial resolution. The large-scale remote sensing semantic segmentation set contains 150 pixel-level annotated GF-2 images, which is labeled in 15 categories."@en ; dct:created "2018-07-16" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Land cover" ; dct:creator "Wuhan University", "Tech. & Eng. Center for Space Utilization, Chinese Academy of Sciences", "China Land Survey and Planning Institute" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "m-brick-kiln dataset"@en ; dct:description "M-brick-kiln dataset is a modified version of the brick-kiln dataset. It was adapted to Geo-Bench. It is used to automatically identifying brick kilns from satellite imagery."@en ; dct:license ; dct:created "2023-08-23" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "Brick kiln detection" ; dct:creator "Technical University of Munich", "ETH Zurich", "University of Maryland", "Stanford University", "Massachusetts Institute of Technology", "University of Amsterdam", "ServiceNow Research", "University of Montreal", "Clark University" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Sen1Floods11 dataset"@en ; dct:description "Sen1Floods11 is a georeferenced dataset to train and test deep learning flood algorithms for Sentinel-1."@en ; dct:created "2020-10-16" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Flood mapping" ; dct:creator "Cloud to Street", "Earth Institute, Columbia University" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Qingdao UAV-borne HSI (QUH) dataset"@en ; dct:description "Qingdao UAV-borne HSI (QUH) dataset consists of three sub-datasets: QUH-Tangdaowan, QUH-Qingyun, and QUH-Pingan, which are available as benchmarks for precise land cover classification. The QUH-Tangdaowan dataset: it contains a variety of irregular land covers, such as four vegetation species and three pavements. The QUH-Qingyun dataset: it has areas obscured by building shadows, including classes such as Trees, Car, and Asphalt road. The QUH-Pingan dataset: it has a relatively regular distribut"@en ; dct:license ; dct:created "2023-07-13" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Hyperspectral", "Land cover" ; dct:creator "Jiangxi Normal University", "China University of Petroleum", "University of New South Wales", "Shandong Provincial Climate Center", "Robert Gordon University" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "KappaZeta dataset"@en ; dct:description "KappaZeta is a cloud mask dataset of 4403 labeled subscenes distributed over terrestrial Northern Europe from April to October 2020. For each month, ten products with stratus clouds, ten products with cumulus clouds and ten products with cirrus clouds were chosen (through active learning method by selecting worst performing sub-tiles after model inference) from different locations. To each label file is associated a Sentinel-2 L1C image. It was created to train the KappaMask model based on CNN."@en ; dct:license ; dct:created "2021-07-13" ; dct:modified "2021-12-20" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "cloud mask", "semantic segmentation", "sentinel-2", "cloud shadow" ; dct:creator "ESA", "KappaZeta", "University of Tartu Estonia" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "SatlasPretrain for semantic segmentation dataset"@en ; dct:description "SatlasPretrain is a large-scale pre-training dataset for remote sensing image understanding. It consists of 302M labels under 137 categories and seven label types: points, polygons, polylines, properties, segmentation labels, regression labels, and classification labels. This description considers the semantic segmentation task."@en ; dct:license ; dct:created "2022-11-28" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Image understanding" ; dct:creator "Allen Institute for AI" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "KappaSet dataset"@en ; dct:description "KappaSet is a cloud mask dataset of 9251 labeled subscenes distributed over the full globe for the whole year 2020 (Winter, Spring, Summer and Autumn products). To each label file is associated a Sentinel-2 L1C image. It was created as an extension of the KappaZeta dataset (itself created to train the KappaMask model based on CNN)."@en ; dct:license ; dct:created "2022-09-23" ; dct:modified "2023-01-22" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "cloud mask", "semantic segmentation", "sentinel-2", "cloud shadow" ; dct:creator "ESA", "KappaZeta", "University of Tartu Estonia" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "AIRS dataset"@en ; dct:description "AIRS (Aerial Imagery for Roof Segmentation) is a public dataset that aims at benchmarking the algorithms of roof segmentation from very-high-resolution aerial imagery. AIRS dataset covers almost the full area of Christchurch, the largest city in the South Island of New Zealand. The photography was taken during the flying seasons of 2015 and 2016."@en ; dct:created "2018-11-16" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Roof segmentation" ; dct:creator "AtlasAI Inc., Canada", "Center for Spatial Information Science, University of Tokyo", "China University of Geosciences, Wuhan", "University of Waterloo, Canada" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "EORSSD dataset"@en ; dct:description "The EORSSD dataset is an extension of ORSSD, adding 1200 ORSIs for a total of 2000 images, with corresponding pixel-level annotations for salient object detection."@en ; dct:created "2020-11-18" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Salient object detection", "Optical remote sensing imagery" ; dct:creator "Beijing Jiaotong University", "City University of Hong Kong", "Beijing Key Laboratory of Advanced Information Science and Network Technology", "School of Electrical and Information Engineering, Tianjin University" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "DSTL dataset"@en ; dct:description "The Dstl Satellite Imagery Feature Detection dataset was released as part of a Kaggle competition organized by the UK Defence Science and Technology Laboratory (Dstl). It consists of very high resolution WorldView-3 satellite images covering multiple geographic areas. The dataset includes detailed polygon annotations for a variety of features."@en ; dct:license ; dct:created "2016-12-15" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Feature detection" ; dct:creator "Defence Science and Technology Laboratory (DSTL)" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Arctic Sea Ice Image Masking dataset"@en ; dct:description "This dataset contains 3392 satellite images of Hudson Bay sea ice in the Canadian Arctic. Images are from Sentinel-2 satellites and are made up of bands 3, 4, and 8 (false color). Each image has an associated mask showing the concentration of sea ice throughout the image."@en ; dct:license ; dct:created "2021-04-28" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Sentinel-2", "Sea ice concentration" ; dct:creator "Alexander Sylvester" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Airbus Wind Turbines dataset"@en ; dct:description "The dataset was created with the objective to build a classifier of satellite images with or without wind turbines."@en ; dct:license ; dct:created "2020-10-27" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "Wind turbine" ; dct:creator "Jeff Faudi", "Airbus DS GEO S.A." ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "AU-AIR dataset"@en ; dct:description "AU-AIR is a A multi-modal unmanned aerial vehicle dataset for low altitude traffic surveillance. It was collected from a UAV platform, and contains over 32K images (obtained from 8 video clips) with bounding box annotations for 8 object categories. It includes RGB images, IMU data, and flight metadata to support deep learning-based perception tasks for traffic surveillance from low altitude."@en ; dct:license ; dct:created "2020-01-31" ; ai4qc:taskType "object detection" ; dcat:keyword "object detection", "traffic surveillance" ; dct:creator "Department of Engineering, Aarhus University" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Parking Lot Database (PKLot) dataset"@en ; dct:description "This database contains 12,417 images captured from two different parking lots (parking1 and parking2) in sunny, cloudy and rainy days. The first parking lot has two different capture angles (parking1a and parking1b). Bounding boxes are defined around parking spots specifying if the spot is vacant or occupied."@en ; dct:license ; dct:created "2015-02-19" ; ai4qc:taskType "object detection" ; dcat:keyword "Object detection", "Parking space" ; dct:creator "Pontifical Catholic University of Parana, Graduate Program in Informatics (PPGIa)", "Federal University of Parana, Department of Informatics" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Inria dataset"@en ; dct:description "Inria is an aerial image labeling dataset that covers a wide range of urban settlement appearances, from different geographic locations. Two semantic classes have been considered: building and bacground."@en ; dct:created "2017-03-13" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Building detection" ; dct:creator "Inria Sophia Antipolis - Mediterranee, TITANE team", "Inria Saclay, TAO team" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "EuroSAT dataset"@en ; dct:description "EuroSAT is a land use and land cover classification dataset based on Sentinel-2 satellite images covering 13 spectral bands and consisting out of 10 classes with in total 27,000 labeled and geo-referenced images."@en ; dct:license ; dct:created "2017-08-31" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "Land cover", "Land use" ; dct:creator "TU Kaiserslautern, Germany", "German Research Center for Artificial Intelligence" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "SMARS dataset"@en ; dct:description "SMARS is a large synthetic dataset containing pairs of scenes with simulated urban changes aimed at training and validating change detection applications, as well as urban segmentation and building extraction tasks."@en ; dct:license ; dct:created "2023-10-06" ; ai4qc:taskType "change detection" ; dcat:keyword "change detection", "synthetic imagery" ; dct:creator "Technical University of Munich", "Remote Sensing Technology Institute, German Aerospace Center (DLR)" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "UAVOD-10 dataset"@en ; dct:description "The UAVOD-10 consists of 844 images and 18,234 instances. These instances are labeled with horizontal bounding boxs (HBB) with VOC format, with 10 possible classes."@en ; dct:created "2022-08-20" ; ai4qc:taskType "object detection" ; dcat:keyword "UAV imagery", "Object detection" ; dct:creator "China University of Geosciences" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "DFC15-multilabel dataset"@en ; dct:description "DFC15 multilabel dataset is produced from GRSS_DFC_2015 - a semantic segmentation dataset. DFC15 dataset is acquired over Zeebrugge with an airborne sensor, which is 300m off the ground. Unlike UCM dataset, where images are assigned with image-level labels, all tiles in DFC15 dataset are labeled in pixel-level."@en ; dct:license ; dct:created "2020-05-06" ; ai4qc:taskType "multilabel classification" ; dcat:keyword "Multilabel classification", "Scene classification" ; dct:creator "Remote Sensing Technology Institute, German Aerospace Center", "Signal Processing in Earth Observation, Technical University of Munich" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Sen4AgriNet dataset"@en ; dct:description "Sen4AgriNet is a Sentinel-2 based time series multi-country benchmark dataset, tailored for agricultural monitoring applications with Machine and Deep Learning."@en ; dct:license ; dct:created "2022-04-27" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Crop classification" ; dct:creator "Institute of Astronomy, Astrophysics, Space Applications & Remote Sensing, National Observatory of Athens" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "UC Merced multilabel dataset"@en ; dct:description "UC Merced multilabel is the multilabel version of the UC Merced dataset. The images were manually extracted from large images from the USGS National Map Urban Area Imagery collection for various urban areas around the country. The pixel resolution of this public domain imagery is 0.3 m."@en ; dct:license ; dct:created "2017-10-30" ; ai4qc:taskType "multilabel classification" ; dcat:keyword "Multilabel classification", "Land use" ; dct:creator "Vision and Image Processing Laboratory, IIT Bombay", "Remote Sensing Laboratory, University of Trento" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Mid-Air dataset"@en ; dct:description "Mid-Air (Montefiore Institute Dataset of Aerial Images and Records) is a multi-purpose synthetic dataset for low altitude drone flights. It provides a large amount of synchronized data corresponding to flight records for multi-modal vision sensors and navigation sensors. More than 420k frames are simulated in various climate conditions."@en ; dct:license ; dct:created "2019-06-01" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Synthetic UAV data" ; dct:creator "University of Liège" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "PASTIS dataset"@en ; dct:description "PASTIS (Panoptic Agricultural Satellite TIme Series) is a benchmark dataset for panoptic and semantic segmentation of agricultural parcels from satellite time series. It contains 2433 patches within the French metropolitan territory with panoptic annotations (instance index + semantic label for each pixel). Each patch is a Sentinel-2 multispectral image time series of variable lentgh."@en ; dct:license ; dct:created "2021-06-22" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Agriculture" ; dct:creator "IGN-ENSG" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "HD-Maps dataset"@en ; dct:description "HD-Maps is a dataset used for proposing a method which enriches road maps by including spatially high-resolution features such as the number and width of lanes, sidewalks, and parking lanes. This method considers the already existing maps together with the aerial and ground photographs."@en ; dct:license ; dct:created "2016-06-27" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Aerial RGB", "Urban semantic segmentation" ; dct:creator "DLR Earth Observation Center" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "AIDER dataset"@en ; dct:description "AIDER stands for Aerial Image Database for Emergency Response applications. The dataset construction involved manually collecting all images for four disaster events, namely Fire/Smoke, Flood, Collapsed Building/Rubble, and Traffic Accidents, as well as one class for the Normal case."@en ; dct:license ; dct:created "2020-12-28" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "Aerial inagery", "Emergency response" ; dct:creator "KIOS Research and Innovation Center of Excellence, University of Cyprus" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Search And Rescue dataset"@en ; dct:description "This is a single class dataset consisting of tiles of satellite imagery labeled with potential 'targets'. It was designed to improve the search for missing persons."@en ; dct:license ; dct:created "2021-07-28" ; ai4qc:taskType "object detection" ; dcat:keyword "Object detection", "Search and rescue" ; dct:creator "Department of Electrical and Computer Engineering, New York University" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "EAGLE dataset"@en ; dct:description "EAGLE stands for oriEnted vehicle detection using Aerial imaGery in real-worLd scEnarios. The EAGLE dataset is used to detect vehicles of different classes including vehicle orientation based on aerial images."@en ; dct:license ; dct:created "2020-07-12" ; ai4qc:taskType "object detection" ; dcat:keyword "Object detection", "Vehicle detection" ; dct:creator "Department of Aerospace, Aeronautics and Geodesy, Technical University of Munich", "Remote Sensing Technology Institute, German Aerospace Center (DLR)" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Aeroscapes dataset"@en ; dct:description "The Aeroscapes dataset was designed to enhance semantic understanding of urban scenes. The dataset comprises 3269 images acquired from 141 video sequences."@en ; dct:license ; dct:created "2018-05-07" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Urban scene" ; dct:creator "Carnegie Mellon University" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "FloodNet dataset"@en ; dct:description "The dataset was created to enhance disaster management and advance the damage assessment process for post-disaster scenarios. FloodNet provides high-resolution UAS imageries with detailed semantic annotations focusing on damage assessment after Hurricane Harvey. The dataset is captured using DJI Mavic Pro quadcopters, providing valuable information for flood damage analysis."@en ; dct:created "2021-03-26" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Flood detection" ; dct:creator "Texas A&M University", "University of Maryland Baltimore County" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Chesapeake Land Cover dataset"@en ; dct:description "This dataset contains high-resolution aerial imagery from the USDA NAIP program, high-resolution land cover labels from the Chesapeake Conservancy, low-resolution land cover labels from the USGS NLCD 2011 dataset, low-resolution multi-spectral imagery from Landsat 8, and high-resolution building footprint masks from Microsoft Bing, formatted to accelerate machine learning research into land cover mapping."@en ; dct:created "2019-06-15" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "land cover", "NAIP" ; dct:creator "Yale University", "University of Southern California", "Stony Brook University", "Microsoft Research", "Chesapeake Conservancy", "Georgia Institute of Technology" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "RFI_AI4QC dataset"@en ; dct:description "This dataset contains a set of labeled RFIs (radio frequency interferences). These interferences are caused by man-made sources and can lead to an artefact in the satellite image, typically a bright rectangular pattern. Bounding boxes were defined around RFI artefacts in Sentinel-1 quick-looks (png images). This dataset can be used to train object detection algorithms to automatically detect these RFIs in a satellite image. A predefined train/test split is available (80% training and 20% testing"@en ; dct:license ; dct:created "2024-07-01" ; dct:modified "2024-09-13" ; ai4qc:taskType "object detection" ; dcat:keyword "object detection", "sentinel-1", "rfi" ; dct:creator "BVLabs" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "RoadNet dataset"@en ; dct:description "The dataset was created for automatic road extraction from remotely sensed images. It covers 21 regions in Ottawa, Canada. Road width ranged from 10 to 80 pixels."@en ; dct:created "2018-10-24" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Road extraction" ; dct:creator "Computer Vision and Remote Sensing Laboratory, Wuhan University" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "COWC Image Classification dataset"@en ; dct:description "The Cars Overhead With Context (COWC) data set is a large set of annotated cars from overhead. All cars in the annotated images have a dot placed at their center. Negative samples are also included in the dataset. It can be used for image classification or object detection/counting tasks. This schema describes the image classification subset of the dataset."@en ; dct:license ; dct:created "2016-01-01" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "Aerial imagery", "Car detection" ; dct:creator "Lawrence Livermore National Laboratory" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "S1_New_Anomalies_AI4QC dataset"@en ; dct:description "This dataset was used in the AI4QC project (Artificial Intelligence for Quality Control), in the context of the detection of new anomalies through unsupervised learning. It consists of 6344 Sentinel-1 images (quick-looks in png format). The dataset was divided into training (80%) and testing (20%). Two criterias were considered for the train/test split: seasonality and geographic location."@en ; dct:license ; dct:created "2024-09-23" ; dct:modified "2024-10-09" ; ai4qc:taskType "unsupervised learning" ; dcat:keyword "sentinel-1", "unsupervised learning", "anomaly detection" ; dct:creator "BVLabs" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "RIT-18 dataset"@en ; dct:description "RIT-18 is a high-resolution multispectral dataset for semantic segmentation."@en ; dct:license ; dct:created "2017-03-19" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Multispectral imagery" ; dct:creator "Chester F. Carlson Center for Imaging Science" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "APKLOT dataset"@en ; dct:description "APKLOT is a dataset for aerial parking block segmentation. It contains roughly 7000 polygons for segmenting parking blocks from the satellite perspective and from the camera perspective."@en ; dct:license ; dct:created "2020-08-10" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Parking space" ; dct:creator "Tecnologico de Monterrey, School of Engineering and Science, Mexico" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Flood Area Segmentation dataset"@en ; dct:description "The dataset contains 290 images of flood hit areas and corresponding mask images showing the water region."@en ; dct:license ; dct:created "2022-12-16" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Flood detection" ; dct:creator "Niyar R. Barman", "Krish Sharma", "Faizal Karim" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Space2Ground dataset"@en ; dct:description "Space2Ground is a multi-level, multi-sensor, multi-modal dataset, annotated with grassland/non-grassland labels for agriculture monitoring. Sentinel-1 SAR data, Sentinel-2 multispectral data and street-level images are combined for the purpose of grassland detection."@en ; dct:license ; dct:created "2022-08-03" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "Grassland detection" ; dct:creator "National Observatory of Athens", "Laboratory of Remote Sensing, National Technical University of Athens" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Swimming Pool And Car Detection dataset"@en ; dct:description "The Swimming Pool And Car Detection dataset contains 6451 images containing swimming pools and cars."@en ; dct:license ; dct:created "2019-03-19" ; ai4qc:taskType "object detection" ; dcat:keyword "Object detection", "Car detection", "Swimming pool detection" ; dct:creator "Kartik Bhartiya" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "C2Seg dataset"@en ; dct:description "C2Seg is a multimodal remote sensing benchmark dataset for cross-city semantic segmentation. The dataset contains satellite imagery from Sentinel-1 SAR (VV, VH), Sentinel-2 multispectral (RGB, NIR), and EnMAP hyperspectral (330 bands), designed for urban semantic segmentation across the cities of Berlin-Augsburg and Beijing-Wuhan."@en ; dct:license ; dct:created "2023-12-15" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Land cover", "Cross-city" ; dct:creator "Heidelberg University", "Univ. Grenoble Alpes", "Chinese Academy of Sciences", "Technical University of Munich" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Hephaestus for semantic segmentation dataset"@en ; dct:description "Hephaestus is a large scale multitask dataset created for InSAR (Interferometric SAR) development. It revolves around the 44 most active volcanoes globally and is focused around global volcano monitoring. This description focuses on the semantic segmentation task."@en ; dct:license ; dct:created "2023-07-03" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "InSAR imagery", "Volcano monitoring" ; dct:creator "National Observatory of Athens", "Harokopio University of Athens" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Crop Type Mapping in Kenya dataset"@en ; dct:description "The dataset contains Sentinel-2 time series of crop types in Kenya. Mapping crop types in smallholder regions faces challenges of small fields, sparse ground truth labels, intercropping, and highly heterogeneous landscapes."@en ; dct:created "2021-09-30" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "Crop type", "Time series" ; dct:creator "Stanford University" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Massachusetts Roads dataset"@en ; dct:description "The Massachusetts Roads Dataset consists of 1171 aerial images of the state of Massachusetts. The dataset covers a wide variety of urban, suburban, and rural regions and covers an area of over 2600 square kilometers. The target maps were generated by rasterizing road centerlines obtained from the OpenStreetMap project."@en ; dct:created "2013-01-01" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Road detection" ; dct:creator "Graduate Department of Computer Science, University of Toronto" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Weakly Supervised Cropland Segmentation Dataset for semantic segmentation"@en ; dct:description "This dataset supports weakly supervised cropland classification in the Midwestern United States. It contains single pixel-level and image-level labels derived from USDA land cover data, which provide high-quality reference labels across the US. This subset considers the pixel-level labels for the semantic segmentation task."@en ; dct:created "2020-01-07" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Cropland" ; dct:creator "Stanford University" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Zurich Summer dataset"@en ; dct:description "The Zurich Summer dataset is a collection of 20 chips (crops), taken from a QuickBird acquisition of the city of Zurich (Switzerland) in August 2002. QuickBird images are composed by 4 channels (NIR-R-G-B) and were pansharpened to the PAN resolution of about 0.62 cm GSD. We manually annotated 8 different urban and periurban classes : Roads, Buildings, Trees, Grass, Bare Soil, Water, Railways and Swimming pools."@en ; dct:license ; dct:created "2015-06-07" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Urban semantic segmentation", "QuickBird satellite images" ; dct:creator "University of Edinburgh" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Oil and Gas Storage Tank dataset"@en ; dct:description "The Oil and Gas Storage Tank (OGST) dataset contains 760 satellite image tiles. Each tile is associated with bounding box labels for oil and gas tanks."@en ; dct:license ; dct:created "2020-02-17" ; ai4qc:taskType "object detection" ; dcat:keyword "Object detection", "Oil/gas tank detection" ; dct:creator "Alberta Geological Survey, University of Alberta" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "SPARCS dataset"@en ; dct:description "This dataset was originally created by M. Joseph Hughes, Oregon State University, and was derived manually from Pre-Collection Landsat 8 scenes. Its purpose was to validate cloud and cloud shadow masking derived from the Spatial Procedures for Automated Removal of Cloud and Shadow (SPARCS) algorithm."@en ; dct:license ; dct:created "2016-03-18" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Landsat 8", "Cloud validation masks" ; dct:creator "Oregon State University" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "CloudSEN12 dataset"@en ; dct:description "Mutli-temporal global dataset created to foster research in cloud and cloud shadow detection. The dataset contains three type of annotaions: high-quality (2000 ROI), scribble (2000 ROI) and no-annotation (5880 ROI). Each ROI contains 5 patches with different cloud covers: clear (0%), low-cloudy (1% - 25%), almost clear (25% - 45%), mid-cloudy (45% - 65%), cloudy (65% >)."@en ; dct:license ; dct:created "2022-11-28" ; dct:modified "2023-05-18" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "cloud detection", "sentinel-2" ; dct:creator "ESA", "FIGMMG (Lima, Peru)", "Z_GIS (Salzburg, Austria)", "the ISP (Valencia, Spain)", "the aiPUCP (Lima, Peru)." ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "COWC-M Image Classification dataset"@en ; dct:description "The Cars Overhead With Context (COWC) data set is a large set of annotated cars from overhead. The COWC-M dataset extends the original COWC dataset by labeling the cars with types. Each car is now labeled as either Sedan, Pickup, Other or Unknown. It can be used for image classification or object detection tasks. This schema describes the image classification subset of the dataset."@en ; dct:license ; dct:created "2019-04-22" ; ai4qc:taskType "multiclass classification" ; dcat:keyword "Multiclass classification", "Aerial imagery", "Car detection" ; dct:creator "Lawrence Livermore National Laboratory" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "SSDD dataset"@en ; dct:description "SAR Ship Detection Dataset (SSDD) is the first open dataset that is widely used to research state-of-the-art technology of ship detection from Synthetic Aperture Radar (SAR) imagery based on deep learning. The SAR ship detection dataset (SSDD) datasets include SAR images with different resolutions, polarizations, sea conditions, large sea areas, and beaches. This dataset is a benchmark for researchers to evaluate their approaches."@en ; dct:license ; dct:created "2017-12-01" ; ai4qc:taskType "object detection" ; dcat:keyword "Object detection", "Ship detection", "Sentinel-1" ; dct:creator "University of Electronic Science and Technology of China" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "SeasoNet dataset"@en ; dct:description "This dataset consists of 1759830 multi-spectral image patches from the Sentinel-2 mission, annotated with image- and pixel-level land cover and land usage labels from the German land cover model LBM-DE2018 with land cover classes based on the CORINE Land Cover database 2018. It includes pixel synchronous examples from each of the four seasons, plus an additional snowy set, spanning the time from April 2018 to February 2019."@en ; dct:license ; dct:created "2022-07-19" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Sentinel-2", "Seasonal scene classification" ; dct:creator "TU Dortmund University" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "CITY-OSM dataset"@en ; dct:description "The dataset was developed for semantic segmentation of very high resolution aerial images to be used for automatic map generation. It is limited to buildings and roads with a total of 1671 images and masks. Aerial images are downloaded from Google Maps and geographic coordinates of buildings and roads are downloaded from OSM."@en ; dct:license ; dct:created "2017-07-21" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Online maps", "Urban semantic segmentation" ; dct:creator "ETH Zurich" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "AgriFieldNet dataset"@en ; dct:description "This dataset contains crop types of agricultural fields in four states of Uttar Pradesh, Rajasthan, Odisha and Bihar in northern India. There are 13 different classes in the dataset. The dataset is split to train and test collections as part of the AgriFieldNet India Competition."@en ; dct:license ; dct:created "2022-09-07" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Sentinel-2", "Crop type" ; dct:creator "Radiant Earth Foundation" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Open-Canopy dataset"@en ; dct:description "Open-Canopy is an open-access and country-scale benchmark for very high resolution (1.5 m) canopy height estimation. It can be used for canopy height change detection between two images taken at different years."@en ; dct:license ; dct:created "2024-12-11" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Canopy height change" ; dct:creator "LSCE/IPSL, CEA-CNRS-UVSQ", "CNRS & École Normale Supérieure", "LIGM, Ecole des Ponts, IP Paris CNRS, UGE", "INRAE, BEF", "EFEO", "LIF, IGN, ENSG", "Kayrros" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "BH-Pools dataset"@en ; dct:description "BH-Pools contains annotated swimming pools over Belo Horizonte in Brazil. The data was acquired through the Google Earth Pro tool. The images were exported from an eye altitude of 330 meters. The dataset consists of 200 images of 8 different neighbourhoods and contains 3980 annotated pools."@en ; dct:created "2020-07-29" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Google Earth", "Pool detection" ; dct:creator "Department of Computer Science, Universidade Federal de Minas Gerais, Brazil" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "Forest Damages Larch Casebearer - Tree classification dataset"@en ; dct:description "The dataset was created to monitor forest areas affected by the larch casebearer, which is a moth that mainly attacks larch trees and has caused significant damage in Sweden. The original dataset contains 1543 images taken from two drone flying occasions over five affected areas in Västergötland, Sweden. The data set is structured in 10 batches, numbered 1 to 10. This subset of the original dataset is refered to as 'Tree classification'. It contains the batches 6 to 10, with 'Larch trees' and 'O"@en ; dct:license ; dct:created "2021-04-01" ; ai4qc:taskType "object detection" ; dcat:keyword "Object detection", "Tree classification" ; dct:creator "Swedish Forest Agency" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] . a dcat:Dataset, schema:Dataset ; dct:title "DFC2022 dataset"@en ; dct:description "The 2022 IEEE GRSS Data Fusion Contest, organized by the Image Analysis and Data Fusion Technical Committee, aims to promote research on semi-supervised learning. The overall objective is to build models that are able to leverage a large amount of unlabelled data while only requiring a small number of annotated training samples."@en ; dct:license ; dct:created "2022-07-03" ; ai4qc:taskType "semantic segmentation" ; dcat:keyword "Semantic segmentation", "Semi-supervised learning" ; dct:creator "German Aerospace Center", "ESA Centre for Earth Observation", "Université de Bretagne-Sud", "National Research Council", "Valeo.ai", "ONERA", "University of Twente" ; dcat:distribution [ a dcat:Distribution ; dcat:downloadURL ; dcat:accessURL ] .