Creating xBD: A Dataset for Assessing Building Damage from Satellite Imagery

Abstract

We present a preliminary report for xBD, a new large-scale dataset for the advancement of change detection and building damage assessment for humanitarian assistance and disaster recovery research.<br>Logistics, resource planning, and damage estimation are difficult tasks after a disaster, and putting first responders into post-disaster situations is dangerous and costly.<br>Using passive methods, such as analysis on satellite imagery, to perform damage assessment saves manpower, lowers risk, and expedites an otherwise dangerous process.<br>xBD provides pre- and post-event multi-band satellite imagery from a variety of disaster events with building polygons, classification labels for damage types, ordinal labels of damage level, and corresponding satellite metadata.<br>Furthermore, the dataset contains bounding boxes and labels for environmental factors such as fire, water, and smoke.<br>xBD will be the largest building damage assessment dataset to date, containing $\sim$700,000 building annotations across over 5,000 km\textsuperscript{2} of imagery from 15 countries.