QuickQuakeBuildings: Post-Earthquake SAR-Optical Dataset for Quick Damaged-Building Detection

Quick and automated earthquake-damaged building detection from postevent satellite imagery is crucial, yet it is challenging due to the scarcity of training data required for developing robust algorithms. This letter presents the first dataset dedicated to detecting earthquake-damaged buildings from postevent very high-resolution (VHR) synthetic aperture radar (SAR) and optical imagery. Utilizing open satellite imagery and annotations acquired for the 2023 Turkey-Syria earthquakes, we deliver a dataset of co-registered building footprints and satellite images of both SAR and optical data, encompassing more than 4000 buildings. The task of the 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 explanations and visualization are made available at
https://github.com/ya0-sun/PostEQ-SARopt-BuildingDamage
.