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

Understand

Rapid damage assessment is of crucial importance to emergency responders during hurricane events, however, the evaluation process is often slow, labor-intensive, costly, and error-prone.

• New advances in computer vision and remote sensing open possibilities to observe the Earth at a different scale.
• However, substantial pre-processing work is still required in order to apply state-of-the-art methodology for emergency response.
• To enable the comparison of methods for automatic detection of damaged buildings from post-hurricane remote sensing imagery taken from both airborne and satellite sensors, this paper presents the development of benchmark datasets from publicly available data.