xBD: A Dataset for Assessing Building Damage from Satellite Imagery

Abstract

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.

9 pages, 10 figures

References in corpus (1)

• Benchmark Dataset for Automatic Damaged Building Detection from Post-Hurricane Remotely Sensed Imagery

Cited by in corpus (6)

• Building Disaster Damage Assessment in Satellite Imagery with Multi-Temporal Fusion
• Assessing Post-Disaster Damage from Satellite Imagery using Semi-Supervised Learning Techniques
• Assessing out-of-domain generalization for robust building damage detection
• Cross-directional Feature Fusion Network for Building Damage Assessment from Satellite Imagery
• SustainBench: Benchmarks for Monitoring the Sustainable Development Goals with Machine Learning
• Characterizing Human Explanation Strategies to Inform the Design of Explainable AI for Building Damage Assessment