SpaceNet 8: Winning Approaches to Multi-Class Feature Segmentation from Satellite Imagery for Flood Disasters

The development of algorithms to assess the effects of natural disasters plays an integral role in response efforts. There is a growing opportunity to leverage remote sensing data and computer vision to quickly analyze the scale of damage and organize a humanitarian response when extreme weather events occur. By automating the process of identifying damage to roads and infrastructure, we can significantly reduce response time, directing relief efforts on a time scale of minutes or hours rather than days. The SpaceNet 8 challenge featured a complex multi-class segmentation problem in the context of flood detection from remote sensing imagery. Competitors were tasked with leveraging both pre- and post-flooding event imagery to detect buildings and roads, as well as identify which of these object instances were affected by the flooding event. We examine the outcome of the SpaceNet 8 challenge and present an overview of the competition and a deeper look at the top-performing submissions.