When disaster strikes, speed is critical. The time it takes to properly assess damage in the wake of a major event can be the difference between life and death. The Defense Innovation Unit’s xView2 Challenge seeks to automate post-disaster damage assessment and speed up emergency response, inviting machine learning experts to develop computer vision algorithms for an opportunity to win part of a $150,000 prize.

Participants’ algorithms should be designed to analyze satellite and aerial imagery in order to localize and categorize various types of building damage caused by natural disasters. Their solutions will be tested against a new dataset, xBD. Created by experts from academia and industry, xBD is currently the largest and most diverse annotated building damage dataset, allowing machine learning/artificial intelligence (ML/AI) practitioners to generate and test models for automating building damage assessments. The xView 2 Challenge partners represent a first-of-its-kind coalition between the artificial intelligence and disaster response communities.

Learn more about how to be part of this group to solve humanitarian assistance and disaster relief challenges using AI/ML at www.xview2.org.