This python codebase and set of Jupyter notebooks presents a framework for semi-automated building damage assessment due to earthquakes from remote sensing data and other supplementary datasets including high-resolution building inventories, while also leveraging recent advances in machine-learning algorithms.
Large files including the DPM .tif files and building footprint geopackages exceeding 100 MB are not stored in this repository due to GitHub's file size restrictions. These files can be downloaded from their original sources as listed in the Data Availability section of the manuscript below.
Rao, A., Jung, J., Silva, V., Molinario, G., and Yun, S.-H. (2023). Earthquake Building Damage Detection based on Synthetic Aperture Radar Imagery and Machine Learning, Natural Hazards and Earth System Sciences, https://doi.org/10.5194/nhess-23-789-2023.