The Semantic Segmentation Of Remote Sensing Imagery

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.

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. Source: Scaling-up Remote Sensing Segmentation Dataset with Segment Anything Model Image Source: Scaling-up Remote Sensing Segmentation Dataset with Segment Anything Model

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. Source: SpaceNet 6: Multi-Sensor All Weather Mapping Dataset

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.

A new benchmark dataset of webcam images, Photi-LakeIce, from multiple cameras and two different winters, along with pixel-wise ground truth annotations. Source: Lake Ice Monitoring with Webcams and Crowd-Sourced Images

The DRIFT dataset includes 25k image patches collected in five European countries sourced from aerial and nanosatellite image archives. Each image patch is associated with three target variables to predict: 1. Canopy height: average height value for pixels containing woody vegetation. 2. Tree count: number of overstory (visible from an overhead perspective) trees in the images. 3. Tree cover fraction: percentage of the image being covered by overstory tree crowns. The DRIFT dataset includes significant shifts between label and visual distributions due to sensor and area differences. Furthermore, vegetation tends to grow to fit the local climate, therefore introducing concept drift in the data: same tree species may appear differently in different subsets. The label distribution also varies among different subsets (countries). The dataset is a good choice for: * image-level regression * domain adaption for regression * remote sensing for forest applications

UV6K is a high-resolution remote sensing urban vehicle segmentation dataset. - Images: 6,313 - Vehicle: 245,141 - Resolution: 0.1m - Image Size: 1024x1024