Benchmark Dataset for Automatic Damaged Building Detection from Post-Hurricane Remotely Sensed Imagery
This provides a scalable framework and datasets for researchers to train and test object detection models, addressing the need for rapid emergency response in hurricane-affected areas, though it is incremental as it builds on existing data and methods.
This paper tackles the problem of slow and labor-intensive damage assessment after hurricanes by developing benchmark datasets for automatic damaged building detection from post-hurricane remote sensing imagery, resulting in publicly shared datasets for the Greater Houston area after Hurricane Harvey in 2017.
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. The major contributions of this work include (1) a scalable framework for creating benchmark datasets of hurricane-damaged buildings and (2) public sharing of the resulting benchmark datasets for Greater Houston area after Hurricane Harvey in 2017. The proposed approach can be used to build other hurricane-damaged building datasets on which researchers can train and test object detection models to automatically identify damaged buildings.