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.
However, emergency responders must often navigate disruptions to local communication and transportation infrastructure, making accurate assessments dangerous, difficult and slow. And while satellite and aerial imagery offer less risky alternatives that cover more ground, analysts must still conduct manual, time-intensive assessments of images.
The Defense Innovation Unit’s xView2 Challenge seeks to automate post-disaster damage assessment. DIU is challenging machine learning experts to develop computer vision algorithms that will speed up analysis of satellite and aerial imagery by localizing and categorizing various types of building damage caused by natural disasters.