This chipped training dataset is over Accra and includes high-resolution imagery (.tif format) and corresponding building footprint vector labels (.geojson format) in 256 x 256 pixel tile/label pairs.
Based on the Spatially-informed Gaussian process regression (Si-GPR) and open-access Sentinel-1 data, this study developed a 1 km × 1km resolution building height dataset across China in 2017.
The annotated point clouds were generated to train the weakly supervised semantic segmentation algorithm Semantic Query Network (SQN) to classify point clouds [1]. The dataset covers 16 tiles of airbo
High resolution land cover dataset for Baltimore City, MD. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other pa
Berlin-Urban-Gradient is a ready-to-use imaging spectrometry dataset for multi-scale unmixing and hard classification analyses in urban environments. The dataset comprises two...
