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"schema:PropertyValue", "schema:name": "rapidai4eo_Natural grassland" }, { "@type": "schema:PropertyValue", "schema:name": "rapidai4eo_Rice fields" }, { "@type": "schema:PropertyValue", "schema:name": "rapidai4eo_Water bodies" }, { "@type": "schema:PropertyValue", "schema:name": "rapidai4eo_Peatbogs" }, { "@type": "schema:PropertyValue", "schema:name": "rapidai4eo_Salines" }, { "@type": "schema:PropertyValue", "schema:name": "rapidai4eo_Non-irrigated arable land" }, { "@type": "schema:PropertyValue", "schema:name": "rapidai4eo_Construction sites" }, { "@type": "schema:PropertyValue", "schema:name": "rapidai4eo_Permanently irrigated land" }, { "@type": "schema:PropertyValue", "schema:name": "rapidai4eo_Complex cultivation patterns" }, { "@type": "schema:PropertyValue", "schema:name": "rapidai4eo_Estuaries" }, { "@type": "schema:PropertyValue", "schema:name": "rapidai4eo_Coniferous forest" }, { "@type": "schema:PropertyValue", "schema:name": "rapidai4eo_Glaciers and perpetual snow" }, { "@type": "schema:PropertyValue", "schema:name": "rapidai4eo_Vineyards" }, { "@type": "schema:PropertyValue", "schema:name": "rapidai4eo_Fruit trees and berry plantations" }, { "@type": "schema:PropertyValue", "schema:name": "rapidai4eo_Moors and heathland" }, { "@type": "schema:PropertyValue", "schema:name": "rapidai4eo_Burnt areas" }, { "@type": "schema:PropertyValue", "schema:name": "rapidai4eo_Sport and leisure facilities" }, { "@type": "schema:PropertyValue", "schema:name": "rapidai4eo_Airports" }, { "@type": "schema:PropertyValue", "schema:name": "rapidai4eo_Agro-forestry areas" } ], "dcat:distribution": [ { "@type": "dcat:Distribution", "dcat:downloadURL": [ "https://rapidai4eo.source.coop/" ], "dcat:accessURL": [ "https://rapidai4eo.source.coop/" ], "dcat:mediaType": "image/tiff", "dcat:byteSize": "~4831838208000" } ] }, { "@id": "https://eodata.bvlabs.ai/ai4eo/datasets/cowc_m_object_detection/", "@type": [ "dcat:Dataset", "schema:Dataset" ], "dct:title": "COWC-M Object 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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.", "dct:created": "2019-04-22", "ai4qc:taskType": "object detection", "dct:license": { "@id": "https://github.com/LLNL/cowc/blob/master/LICENSE" }, "dcat:keyword": [ "Aerial imagery", "Car detection" ], "dct:creator": [ { "@type": "schema:Organization", "schema:name": "Lawrence Livermore National Laboratory" } ], "schema:citation": [ "https://www.kaggle.com/datasets/nasdevs/cowc-m-dataset-detection-patches-256x256" ], "schema:variableMeasured": [ { "@type": "schema:PropertyValue", "schema:name": "cowc_m_object_detection_sedan" }, { "@type": "schema:PropertyValue", "schema:name": "cowc_m_object_detection_pickup" }, { "@type": "schema:PropertyValue", "schema:name": "cowc_m_object_detection_unknown" }, { "@type": "schema:PropertyValue", "schema:name": "cowc_m_object_detection_other" } ], "dcat:distribution": [ { "@type": "dcat:Distribution", "dcat:downloadURL": [ "https://gdo152.llnl.gov/cowc/download/" ], "dcat:accessURL": [ "https://gdo152.llnl.gov/cowc/" ], "dcat:mediaType": "text/txt", "dcat:byteSize": "10737418240" } ] }, { "@id": "https://eodata.bvlabs.ai/ai4eo/datasets/rsc11/", "@type": [ "dcat:Dataset", "schema:Dataset" ], "dct:title": "RSC11 dataset", "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. 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The goal of AID is to advance the state-of-the-arts in scene classification of remote sensing images. 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This dataset contains a total of 5604 high-resolution SAR images and 16951 ship instances.", "dct:created": "2020-06-29", "ai4qc:taskType": "object detection", "dct:license": { "@id": "https://www.gnu.org/licenses/gpl-3.0.html" }, "dcat:keyword": [ "Object detection", "SAR imagery", "Ship detection" ], "dct:creator": [ { "@type": "schema:Organization", "schema:name": "University of Electronic Science and Technology of China" } ], "schema:citation": [ "https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9127939" ], "schema:variableMeasured": [ { "@type": "schema:PropertyValue", "schema:name": "hrsid_ship" } ], "dcat:distribution": [ { "@type": "dcat:Distribution", "dcat:downloadURL": [ "https://github.com/chaozhong2010/HRSID" ], "dcat:accessURL": [ "https://github.com/chaozhong2010/HRSID" ], "dcat:mediaType": "image/jpg", "dcat:byteSize": "608910165" } ] }, { "@id": "https://eodata.bvlabs.ai/ai4eo/datasets/valid_semantic_segmentation/", "@type": [ "dcat:Dataset", "schema:Dataset" ], "dct:title": "VALID Semantic Segmentation dataset", "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.", "dct:created": "2020-01-27", "ai4qc:taskType": "semantic segmentation", "dcat:keyword": [ "Semantic segmentation", "Aerial imagery" ], "dct:creator": [ { "@type": "schema:Organization", "schema:name": "State Key Laboratory of Intelligent Technology and Systems, Tsinghua University" } ], "schema:citation": [ "https://ieeexplore.ieee.org/document/9197186" ], "schema:variableMeasured": [ { "@type": "schema:PropertyValue", "schema:name": "valid_semantic_segmentation_harbor" }, { "@type": "schema:PropertyValue", "schema:name": "valid_semantic_segmentation_tunnel" }, { "@type": "schema:PropertyValue", "schema:name": "valid_semantic_segmentation_animal" }, { "@type": "schema:PropertyValue", "schema:name": "valid_semantic_segmentation_person" }, { "@type": "schema:PropertyValue", "schema:name": "valid_semantic_segmentation_pool" }, { "@type": "schema:PropertyValue", "schema:name": "valid_semantic_segmentation_ship" }, { "@type": "schema:PropertyValue", "schema:name": "valid_semantic_segmentation_high obstacle" }, { "@type": "schema:PropertyValue", "schema:name": "valid_semantic_segmentation_stone" }, { "@type": "schema:PropertyValue", "schema:name": "valid_semantic_segmentation_garbage bin" }, { "@type": "schema:PropertyValue", "schema:name": "valid_semantic_segmentation_fence" }, { "@type": "schema:PropertyValue", "schema:name": "valid_semantic_segmentation_bridge" }, { "@type": "schema:PropertyValue", "schema:name": "valid_semantic_segmentation_plane" }, { "@type": "schema:PropertyValue", "schema:name": "valid_semantic_segmentation_chair" }, { "@type": "schema:PropertyValue", "schema:name": "valid_semantic_segmentation_building" }, { "@type": "schema:PropertyValue", "schema:name": "valid_semantic_segmentation_telegraph pole" }, { "@type": "schema:PropertyValue", "schema:name": "valid_semantic_segmentation_pier rubble" }, { "@type": "schema:PropertyValue", "schema:name": "valid_semantic_segmentation_sign" }, { "@type": "schema:PropertyValue", "schema:name": "valid_semantic_segmentation_bus stop" }, { "@type": "schema:PropertyValue", "schema:name": "valid_semantic_segmentation_water" }, { "@type": "schema:PropertyValue", "schema:name": "valid_semantic_segmentation_ice" }, { "@type": "schema:PropertyValue", "schema:name": "valid_semantic_segmentation_pavement" }, { "@type": "schema:PropertyValue", "schema:name": "valid_semantic_segmentation_small vehicle" }, { "@type": "schema:PropertyValue", "schema:name": "valid_semantic_segmentation_tree" }, { "@type": "schema:PropertyValue", "schema:name": "valid_semantic_segmentation_land" }, { "@type": "schema:PropertyValue", "schema:name": "valid_semantic_segmentation_plant" }, { "@type": "schema:PropertyValue", "schema:name": "valid_semantic_segmentation_traffic light" }, { "@type": "schema:PropertyValue", "schema:name": "valid_semantic_segmentation_low obstacle" }, { "@type": "schema:PropertyValue", "schema:name": "valid_semantic_segmentation_large vehicle" }, { "@type": "schema:PropertyValue", "schema:name": "valid_semantic_segmentation_road" }, { "@type": "schema:PropertyValue", "schema:name": "valid_semantic_segmentation_lamp" } ], "dcat:distribution": [ { "@type": "dcat:Distribution", "dcat:downloadURL": [ "https://drive.google.com/drive/folders/1nKQqR92NqNEWj-KXvxAKekFdQikrotLj" ], "dcat:accessURL": [ "https://sites.google.com/view/valid-dataset" ], "dcat:mediaType": "image/png", "dcat:byteSize": "16523812929.54" } ] }, { "@id": "https://eodata.bvlabs.ai/ai4eo/datasets/spacenet6/", "@type": [ "dcat:Dataset", "schema:Dataset" ], "dct:title": "SpaceNet-6 dataset", "dct:description": "SpaceNet-6 is a multi-sensor all weather mapping dataset. 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It contains 19 classes of meaningful scenes in high-resolution satellite imagery. For each class, there are about 50 samples. 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It revolves around the 44 most active volcanoes globally and is focused around global volcano monitoring. This description focuses on the image classification task.", "dct:created": "2023-07-03", "ai4qc:taskType": "multiclass classification", "dct:license": { "@id": "https://opensource.org/license/mit" }, "dcat:keyword": [ "Multiclass classification", "InSAR imagery", "Volcano monitoring" ], "dct:creator": [ { "@type": "schema:Organization", "schema:name": "National Observatory of Athens" }, { "@type": "schema:Organization", "schema:name": "Harokopio University of Athens" } ], "schema:citation": [ "https://arxiv.org/abs/2204.09435" ], "schema:variableMeasured": [ { "@type": "schema:PropertyValue", "schema:name": "hephaestus_image_classification_Mogi" }, { "@type": "schema:PropertyValue", "schema:name": "hephaestus_image_classification_Sill" }, { "@type": "schema:PropertyValue", "schema:name": "hephaestus_image_classification_Dyke" }, { "@type": "schema:PropertyValue", "schema:name": "hephaestus_image_classification_Unidentified" }, { "@type": "schema:PropertyValue", "schema:name": "hephaestus_image_classification_Spheroid" }, { "@type": "schema:PropertyValue", "schema:name": "hephaestus_image_classification_Earthquake" } ], "dcat:distribution": [ { "@type": "dcat:Distribution", "dcat:downloadURL": [ "https://www.dropbox.com/scl/fi/qx99z7tlx6994r7zn4fgh/Hephaestus_Classification.zip?rlkey=lnnmakeu50au0r50yjbjonivx&e=1&dl=0" ], "dcat:accessURL": [ "https://github.com/Orion-AI-Lab/Hephaestus" ], "dcat:mediaType": "text/json", "dcat:byteSize": "11918534246.4" } ] }, { "@id": "https://eodata.bvlabs.ai/ai4eo/datasets/msbc/", "@type": [ "dcat:Dataset", "schema:Dataset" ], "dct:title": "MSBC dataset", "dct:description": "The multisource built-up change (MSBC) dataset contains multispectral, SAR, and VHR multisource data for change detection.", "dct:created": "2022-06-08", "ai4qc:taskType": "change detection", "dct:license": { "@id": "https://ieeexplore.ieee.org/document/9791854 - 'Both of the datasets be made openly available for all research needs'" }, "dcat:keyword": [ "change detection", "multisource imagery" ], "dct:creator": [ { "@type": "schema:Organization", "schema:name": "Division of Landscape Architecture, University of Hong Kong" }, { "@type": "schema:Organization", "schema:name": "Guangdong Provincial Key Laboratory of Urbanization and Geo-Simulation, Sun Yat-sen University" } ], "schema:citation": [ "https://ieeexplore.ieee.org/document/9791854" ], "schema:variableMeasured": [ { "@type": "schema:PropertyValue", "schema:name": "msbc_no change" }, { "@type": "schema:PropertyValue", "schema:name": "msbc_change" } ], "dcat:distribution": [ { "@type": "dcat:Distribution", "dcat:downloadURL": [ "https://drive.google.com/file/d/1iFp3jMFYwbk0iP4rN3hCIUwmnNxmDewz/view" ], "dcat:accessURL": [ "https://github.com/Lihy256/MSCDUnet" ], "dcat:mediaType": "image/tiff", "dcat:byteSize": "4342272000" } ] }, { "@id": "https://eodata.bvlabs.ai/ai4eo/datasets/whuaerial/", "@type": [ "dcat:Dataset", "schema:Dataset" ], "dct:title": "WHUAerial dataset", "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.", "dct:created": "2018-08-22", "ai4qc:taskType": "semantic segmentation", "dct:license": { "@id": "https://creativecommons.org/licenses/by-sa/4.0/" }, "dcat:keyword": [ "Semantic segmentation", "Building detection" ], "dct:creator": [ { "@type": "schema:Organization", "schema:name": "School of Remote sensing and Information Engineering, Wuhan University" }, { "@type": "schema:Organization", "schema:name": "Department of Physical Geography, Utrecht University" } ], "schema:citation": [ "https://ieeexplore.ieee.org/document/8444434" ], "schema:variableMeasured": [ { "@type": "schema:PropertyValue", "schema:name": "whuaerial_background" }, { "@type": "schema:PropertyValue", "schema:name": "whuaerial_building" } ], "dcat:distribution": [ { "@type": "dcat:Distribution", "dcat:downloadURL": [ "https://www.kaggle.com/datasets/rameezakther/whu-building-rooftop-dataset" ], "dcat:accessURL": [ "https://www.kaggle.com/datasets/rameezakther/whu-building-rooftop-dataset", "http://gpcv.whu.edu.cn/data/" ], "dcat:mediaType": "image/tiff", "dcat:byteSize": "5475663872" } ] }, { "@id": "https://eodata.bvlabs.ai/ai4eo/datasets/parallax_ai4qc/", "@type": [ "dcat:Dataset", "schema:Dataset" ], "dct:title": "Parallax_AI4QC dataset", "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 sets of labels are available: rotated_bboxes and straight_bboxes. This comes from the fact that parallax artefacts are often rotated on the image. The rotated bounding boxes will contain only the rotated parallax artefact and not the background. However since most machine learning models don't account for a rotation value, straight bounding boxes were created as well. These bounding boxes will contain some of the background as well as the parallax. A predefined train/test split is available (80% training and 20% testing). 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The mask is an RGB image with 7 classes following the Anderson Classification.", "dct:created": "2018-05-17", "ai4qc:taskType": "semantic segmentation", "dct:license": { "@id": "http://deepglobe.org/docs/DeepGlobe_Rules_3_2.pdf, http://deepglobe.org/docs/CVPR_InternalUseLicenseAgreement_07-11-18.pdf, http://deepglobe.org/docs/Annotation%20License%20Agreement.pdf" }, "dcat:keyword": [ "Semantic segmentation", "Land cover" ], "dct:creator": [ { "@type": "schema:Organization", "schema:name": "DigitalGlobe" }, { "@type": "schema:Organization", "schema:name": "The MIT Media Lab" }, { "@type": "schema:Organization", "schema:name": "Wageningen University" }, { "@type": "schema:Organization", "schema:name": "Facebook" }, { "@type": "schema:Organization", "schema:name": "CosmiQ Works" } ], "schema:citation": [ "https://arxiv.org/abs/1805.06561" ], "schema:variableMeasured": [ { "@type": "schema:PropertyValue", "schema:name": "deepglobe_land_cover_agriculture-land" }, { "@type": "schema:PropertyValue", "schema:name": "deepglobe_land_cover_urban-land" }, { "@type": "schema:PropertyValue", "schema:name": "deepglobe_land_cover_forest-land" }, { "@type": "schema:PropertyValue", "schema:name": "deepglobe_land_cover_rangeland" }, { "@type": "schema:PropertyValue", "schema:name": "deepglobe_land_cover_water" }, { "@type": "schema:PropertyValue", "schema:name": "deepglobe_land_cover_barren-land" }, { "@type": "schema:PropertyValue", "schema:name": "deepglobe_land_cover_unknown" } ], "dcat:distribution": [ { "@type": "dcat:Distribution", "dcat:downloadURL": [ "https://www.kaggle.com/datasets/balraj98/deepglobe-land-cover-classification-dataset" ], "dcat:accessURL": [ "http://deepglobe.org/" ], "dcat:mediaType": "image/jpg", "dcat:byteSize": "3221225472" } ] }, { "@id": "https://eodata.bvlabs.ai/ai4eo/datasets/s2looking/", "@type": [ "dcat:Dataset", "schema:Dataset" ], "dct:title": "S2Looking dataset", "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.", "dct:created": "2021-12-27", "ai4qc:taskType": "change detection", "dct:license": { "@id": "https://creativecommons.org/publicdomain/zero/1.0/" }, "dcat:keyword": [ "change detection", "building change" ], "dct:creator": [ { "@type": "schema:Organization", "schema:name": "Image Processing Center, Beihang University" }, { "@type": "schema:Organization", "schema:name": "Institute of Resource and Environment, Capital Normal University" }, { "@type": "schema:Organization", "schema:name": "Beijing Institute of Remote Sensing" }, { "@type": "schema:Organization", "schema:name": "School of Microelectronics, Tianjin University" } ], "schema:citation": [ "https://arxiv.org/abs/2107.09244" ], "schema:variableMeasured": [ { "@type": "schema:PropertyValue", "schema:name": "s2looking_new building" }, { "@type": "schema:PropertyValue", "schema:name": "s2looking_demolished building" }, { "@type": "schema:PropertyValue", "schema:name": "s2looking_no-change" } ], "dcat:distribution": [ { "@type": "dcat:Distribution", "dcat:downloadURL": [ "https://drive.google.com/drive/folders/1zzb6hif2hwWx4z8UIMLpMAInkhbmmrFY" ], "dcat:accessURL": [ "https://github.com/S2Looking/Dataset" ], "dcat:mediaType": "image/png", "dcat:byteSize": "10959773696" } ] }, { "@id": "https://eodata.bvlabs.ai/ai4eo/datasets/five_billion_pixels/", "@type": [ "dcat:Dataset", "schema:Dataset" ], "dct:title": "Five Billion Pixels dataset", "dct:description": "Five Billion Pixels is a large-scake land cover classification dataset. 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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 globally distributed and cover the year 2017-2018. The scenes are collected for different seasons and cloud cover types. The dataset is a smaller version of the CESBIO dataset, where the S2 L1C images have been added and the classes modified. The new classes are the following: 0-clear, 1-thick cloud, 2-thin cloud, 3-cloud shadow. The cloud masks are of size 1830 x 1830 pixels with a resolution of 60m. 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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. 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It was created as part of the xView2 Challenge organized by DARPA and the DIUx xView Team. 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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), with training and testing zip folders containing the images and labels for each subset. The data was split according to 3 criterias: RFI over land vs sea, size of the RFI bounding boxes and geographic location.", "dct:created": "2024-07-01", "dct:modified": "2024-09-13", "ai4qc:taskType": "object detection", "dct:license": { "@id": "https://creativecommons.org/licenses/by/4.0/" }, "dcat:keyword": [ "object detection", "sentinel-1", "rfi" ], "dct:creator": [ { "@type": "schema:Organization", "schema:name": "BVLabs" } ], "schema:variableMeasured": [ { "@type": "schema:PropertyValue", "schema:name": "rfi-ai4qc_Other Anomaly" }, { "@type": "schema:PropertyValue", "schema:name": "rfi-ai4qc_RFI" } ], "dcat:distribution": [ { "@type": "dcat:Distribution", "dcat:downloadURL": [ "https://zenodo.org/records/13848248" ], "dcat:accessURL": [ "https://zenodo.org/records/13848248" ], "dcat:mediaType": "text/xml", "dcat:byteSize": "1932735283", "dct:modified": "2024-09-13" } ] }, { "@id": "https://eodata.bvlabs.ai/ai4eo/datasets/roadnet/", "@type": [ "dcat:Dataset", "schema:Dataset" ], "dct:title": "RoadNet dataset", "dct:description": "The dataset was created for automatic road extraction from remotely sensed images. 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Road width ranged from 10 to 80 pixels.", "dct:created": "2018-10-24", "ai4qc:taskType": "semantic segmentation", "dcat:keyword": [ "Semantic segmentation", "Road extraction" ], "dct:creator": [ { "@type": "schema:Organization", "schema:name": "Computer Vision and Remote Sensing Laboratory, Wuhan University" } ], "schema:citation": [ "https://ieeexplore.ieee.org/document/8506600" ], "schema:variableMeasured": [ { "@type": "schema:PropertyValue", "schema:name": "roadnet_no-road" }, { "@type": "schema:PropertyValue", "schema:name": "roadnet_road" } ], "dcat:distribution": [ { "@type": "dcat:Distribution", "dcat:downloadURL": [ "https://drive.google.com/file/d/1GDHy7uwgOswuCDC49OamlNkAxjaITPBI/view" ], "dcat:accessURL": [ "https://github.com/yhlleo/RoadNet" ], "dcat:mediaType": "image/tiff", "dcat:byteSize": "947912704" } ] }, { "@id": "https://eodata.bvlabs.ai/ai4eo/datasets/cowc_image_classification/", "@type": [ "dcat:Dataset", "schema:Dataset" ], "dct:title": "COWC Image Classification dataset", "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.", "dct:created": "2016-01-01", "ai4qc:taskType": "multiclass classification", "dct:license": { "@id": "https://github.com/LLNL/cowc/blob/master/LICENSE" }, "dcat:keyword": [ "Multiclass classification", "Aerial imagery", "Car detection" ], "dct:creator": [ { "@type": "schema:Organization", "schema:name": "Lawrence Livermore National Laboratory" } ], "schema:citation": [ "https://gdo152.llnl.gov/cowc/mundhenk_et_al_eccv_2016.pdf" ], "schema:variableMeasured": [ { "@type": "schema:PropertyValue", "schema:name": "cowc_image_classification_car" }, { "@type": "schema:PropertyValue", "schema:name": "cowc_image_classification_negative" } ], "dcat:distribution": [ { "@type": "dcat:Distribution", "dcat:downloadURL": [ "https://gdo152.llnl.gov/cowc/download/" ], "dcat:accessURL": [ "https://gdo152.llnl.gov/cowc/" ], "dcat:mediaType": "image/png", "dcat:byteSize": "57982058496" } ] }, { "@id": "https://eodata.bvlabs.ai/ai4eo/datasets/S1-new-anomalies-ai4qc/", "@type": [ "dcat:Dataset", "schema:Dataset" ], "dct:title": "S1_New_Anomalies_AI4QC dataset", "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.", "dct:created": "2024-09-23", "dct:modified": "2024-10-09", "ai4qc:taskType": "unsupervised learning", "dct:license": { "@id": "https://creativecommons.org/licenses/by/4.0/" }, "dcat:keyword": [ "sentinel-1", "unsupervised learning", "anomaly detection" ], "dct:creator": [ { "@type": "schema:Organization", "schema:name": "BVLabs" } ], "dcat:distribution": [ { "@type": "dcat:Distribution", "dcat:downloadURL": [ "https://zenodo.org/records/13907111" ], "dcat:accessURL": [ "https://zenodo.org/records/13907111" ], "dcat:mediaType": "image/png", "dcat:byteSize": "1825361100", "dct:modified": "2024-10-09" } ] }, { "@id": "https://eodata.bvlabs.ai/ai4eo/datasets/rit_18/", "@type": [ "dcat:Dataset", "schema:Dataset" ], "dct:title": "RIT-18 dataset", "dct:description": "RIT-18 is a high-resolution multispectral dataset for semantic segmentation.", "dct:created": "2017-03-19", "ai4qc:taskType": "semantic segmentation", "dct:license": { "@id": "https://creativecommons.org/licenses/by/4.0/" }, "dcat:keyword": [ "Semantic segmentation", "Multispectral imagery" ], "dct:creator": [ { "@type": "schema:Organization", "schema:name": "Chester F. 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Each tile is associated with bounding box labels for oil and gas tanks.", "dct:created": "2020-02-17", "ai4qc:taskType": "object detection", "dct:license": { "@id": "https://creativecommons.org/licenses/by/4.0/" }, "dcat:keyword": [ "Object detection", "Oil/gas tank detection" ], "dct:creator": [ { "@type": "schema:Organization", "schema:name": "Alberta Geological Survey, University of Alberta" } ], "schema:citation": [ "https://www.nature.com/articles/s41597-023-02780-1" ], "schema:variableMeasured": [ { "@type": "schema:PropertyValue", "schema:name": "ogst_tank" } ], "dcat:distribution": [ { "@type": "dcat:Distribution", "dcat:downloadURL": [ "https://data.mendeley.com/datasets/bkxj8z84m9/3" ], "dcat:accessURL": [ "https://data.mendeley.com/datasets/bkxj8z84m9/3" ], "dcat:mediaType": "text/xml", "dcat:byteSize": "3061848192" } ] }, { "@id": "https://eodata.bvlabs.ai/ai4eo/datasets/sparcs/", "@type": [ "dcat:Dataset", "schema:Dataset" ], "dct:title": "SPARCS dataset", "dct:description": "This dataset was originally created by M. 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This dataset is a benchmark for researchers to evaluate their approaches.", "dct:created": "2017-12-01", "ai4qc:taskType": "object detection", "dct:license": { "@id": "https://www.apache.org/licenses/LICENSE-2.0" }, "dcat:keyword": [ "Object detection", "Ship detection", "Sentinel-1" ], "dct:creator": [ { "@type": "schema:Organization", "schema:name": "University of Electronic Science and Technology of China" } ], "schema:citation": [ "https://ieeexplore.ieee.org/document/8124934", "https://www.mdpi.com/2072-4292/13/18/3690" ], "schema:variableMeasured": [ { "@type": "schema:PropertyValue", "schema:name": "ssdd_ship" } ], "dcat:distribution": [ { "@type": "dcat:Distribution", "dcat:downloadURL": [ "https://github.com/TianwenZhang0825/Official-SSDD?tab=readme-ov-file" ], "dcat:accessURL": [ "https://github.com/TianwenZhang0825/Official-SSDD?tab=readme-ov-file" ], "dcat:mediaType": "image/jpg", "dcat:byteSize": "1042284544" } ] }, { "@id": "https://eodata.bvlabs.ai/ai4eo/datasets/seasonet/", "@type": [ "dcat:Dataset", "schema:Dataset" ], "dct:title": "SeasoNet dataset", "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. 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There are 13 different classes in the dataset. 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It can be used for canopy height change detection between two images taken at different years.", "dct:created": "2024-12-11", "ai4qc:taskType": "semantic segmentation", "dct:license": { "@id": "https://spdx.org/licenses/etalab-2.0" }, "dcat:keyword": [ "Semantic segmentation", "Canopy height change" ], "dct:creator": [ { "@type": "schema:Organization", "schema:name": "LSCE/IPSL, CEA-CNRS-UVSQ" }, { "@type": "schema:Organization", "schema:name": "CNRS & École Normale Supérieure" }, { "@type": "schema:Organization", "schema:name": "LIGM, Ecole des Ponts, IP Paris CNRS, UGE" }, { "@type": "schema:Organization", "schema:name": "INRAE, BEF" }, { "@type": "schema:Organization", "schema:name": "EFEO" }, { "@type": "schema:Organization", "schema:name": "LIF, IGN, ENSG" }, { "@type": "schema:Organization", "schema:name": "Kayrros" } ], "schema:citation": [ "https://arxiv.org/abs/2407.09392" ], "schema:variableMeasured": [ { "@type": "schema:PropertyValue", "schema:name": "open_canopy_background" }, { "@type": "schema:PropertyValue", "schema:name": "open_canopy_forest" } ], "dcat:distribution": [ { "@type": "dcat:Distribution", "dcat:downloadURL": [ "https://huggingface.co/datasets/AI4Forest/Open-Canopy/tree/main" ], "dcat:accessURL": [ "https://huggingface.co/datasets/AI4Forest/Open-Canopy" ], "dcat:mediaType": "image/tiff", "dcat:byteSize": "454192791552" } ] }, { "@id": "https://eodata.bvlabs.ai/ai4eo/datasets/bh_pools/", "@type": [ "dcat:Dataset", "schema:Dataset" ], "dct:title": "BH-Pools dataset", "dct:description": "BH-Pools contains annotated swimming pools over Belo Horizonte in Brazil. 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