  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.
