Gaofen Image Dataset (GID) is a large-scale land-cover dataset constructed with Gaofen-2 (GF-2) satellite images. This dataset has superiorities over the existing land-cover dataset because of its large coverage, wide distribution, and high spatial resolution. It contains 150 GF-2 images annotated at the pixel level for 5 categories: built-up, farmland, forest, meadow, and water.
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Multi-Sensor All Weather Mapping (MSAW) is a dataset and challenge, which features two collection modalities (both SAR and optical). The dataset and challenge focus on mapping and building footprint extraction using a combination of these data sources. MSAW covers 120 km^2 over multiple overlapping collects and is annotated with over 48,000 unique building footprints labels, enabling the creation and evaluation of mapping algorithms for multi-modal data.
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SAMRS is a remote sensing segmentation dataset which provides object category, location, and instance information that can be used for semantic segmentation, instance segmentation, and object detection, either individually or in combination.
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The Five-Billion-Pixels dataset contains more than 5 billion labeled pixels of 150 high-resolution Gaofen-2 (4 m) satellite images, annotated in a 24-category system covering artificial-constructed, agricultural, and natural classes. 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.
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UV6K is a high-resolution remote sensing urban vehicle segmentation dataset.
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