xBD: A Dataset for Assessing Building Damage from Satellite Imagery
This dataset addresses the need for accurate and timely building damage assessment for humanitarian assistance and disaster recovery, potentially reducing danger to human life by enabling computer vision applications.
The authors tackled the problem of building damage assessment after natural disasters by creating xBD, a large-scale dataset of pre- and post-event satellite imagery with building annotations, resulting in the largest such dataset to date with 850,736 building annotations across 45,362 km².
We present xBD, a new, large-scale dataset for the advancement of change detection and building damage assessment for humanitarian assistance and disaster recovery research. Natural disaster response requires an accurate understanding of damaged buildings in an affected region. Current response strategies require in-person damage assessments within 24-48 hours of a disaster. Massive potential exists for using aerial imagery combined with computer vision algorithms to assess damage and reduce the potential danger to human life. In collaboration with multiple disaster response agencies, xBD provides pre- and post-event satellite imagery across a variety of disaster events with building polygons, ordinal labels of damage level, and corresponding satellite metadata. Furthermore, the dataset contains bounding boxes and labels for environmental factors such as fire, water, and smoke. xBD is the largest building damage assessment dataset to date, containing 850,736 building annotations across 45,362 km\textsuperscript{2} of imagery.