  Quick and automated earthquake-damaged building detection from post-event
satellite imagery is crucial, yet it is challenging due to the scarcity of
training data required to develop robust algorithms. This letter presents the
first dataset dedicated to detecting earthquake-damaged buildings from
post-event very high resolution (VHR) Synthetic Aperture Radar (SAR) and
optical imagery. Utilizing open satellite imagery and annotations acquired
after the 2023 Turkey-Syria earthquakes, we deliver a dataset of coregistered
building footprints and satellite image patches of both SAR and optical data,
encompassing more than four thousand buildings. The task of damaged building
detection is formulated as a binary image classification problem, that can also
be treated as an anomaly detection problem due to extreme class imbalance. We
provide baseline methods and results to serve as references for comparison.
Researchers can utilize this dataset to expedite algorithm development,
facilitating the rapid detection of damaged buildings in response to future
events. The dataset and codes together with detailed explanations and
visualization are made publicly available at
\url{https://github.com/ya0-sun/PostEQ-SARopt-BuildingDamage}.
