ORBITaL-Net Training Library for Building Extraction

The Oak Ridge Building Image and TrAining Label Net (ORBITaL-Net), is a training dataset designed to enable the learning of building detection deep learning models. It consists of over 128,000 individual samples drawn from thousands of separate high resolution satellite images (average resolution 0.47 m). Each sample is a 500x500 pixel patch with accompanying binary label raster with each pixel hand-annotated by expertly trained image analysts as either building or non-building. This dataset has a large degree of geographic and semantic variety, including samples from North America, South America, Africa, the Middle East, and Asia, as well as samples that include a variety of viewing angles, vernacular architecture styles, LU/LC contexts, and atmospheric conditions.