  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.
