Unlike satellite images, which are typically acquired and processed in near-real-time, global
land cover products have historically been produced on an annual basis, often with
substantial lag times between image processing and dataset release. We developed a new
automated approach for globally consistent, high resolution, near real-time (NRT) land use
land cover (LULC) classification leveraging deep learning on 10 m Sentinel-2 imagery. We
utilize a highly scalable cloud-based system to apply this approach and provide an open …