Benchmark Dataset for Automatic Damaged Building Detection from Post-Hurricane Remotely Sensed Imagery
Emergency managers of today grapple with post-hurricane damage assessment that is often labor-intensive, slow,costly, and error-prone. As an important first step towards addressing the challenge, this paper presents the development of benchmark datasets to enable the automatic detection ofdamaged buildings from post-hurricane remote sensing imagerytaken from both airborne and satellite sensors. Our work has two major contributions: (1) we propose a scalable framework to create benchmark datasets of hurricane-damaged buildings and (2) we share publicly the resulting benchmark datasets for Greater Houston area after Hurricane Harvey, 2017. Thebenchmark datasets can be used by other researchers to train and test object detection models which aim to detect the locationof damaged buildings in the vast imagery over affected areas.
Data can be used for object detection algorithms to properly annotate post disaster buildings as either damaged or non damaged aiding disaster response. This dataset contains ESRI Shapefiles of bounding boxes of buildings labeled as either non-damaged or damaged. Those labeled as damaged also have four degrees of damage from minor to catastrophic. Importantly, each bounding box is also indexed to one of the images in the NOAA post Harvey hurricane imagery dataset allowing users to match the bounding boxes with the correct imagery for training the algorithm.