SatlasPretrain: A Large-Scale Dataset for Remote Sensing Image Understanding

This paper presents SATLASPRETRAIN, a remote sensing dataset that is large in both breadth and scale, combining Sentinel-2 and NAIP images with 302M labels under 137 categories and seven label types. We evaluate eight baselines and a proposed method on SATLASPRETRAIN, and find that there is substantial room for improvement in addressing research challenges specific to remote sensing, including processing image time series that consist of images from very different types of sensors, and taking advantage of longrange spatial context