As the world marked the midterm of the Sendai Framework for Disaster Risk Reduction 2015-2030, many countries are still struggling to monitor their climate and disaster risk because of the expensive large-scale survey of the distribution of exposure and physical vulnerability and, hence, are not on track in reducing risks amidst the intensifying effects of climate change. We present an ongoing effort in mapping this vital information using machine learning and time-series remote sensing from publicly available Sentinel-1 SAR GRD and Sentinel-2 Harmonized MSI. We introduce the development of "OpenSendaiBench" consisting of 47 countries wherein most are least developed (LDCs), trained ResNet-50 deep learning models, and demonstrated the region of Dhaka, Bangladesh by mapping the distribution of its informal constructions. As a pioneering effort in auditing global disaster risk over time, this paper aims to advance the area of large-scale risk quantification in informing our collective long-term efforts in reducing climate and disaster risk.

This is the camera-ready paper for the accepted poster at the 2nd Machine Learning for Remote Sensing Workshop, 12th International Conference on Learning Representations (ICLR) in Vienna, Austria, on the 11th of May 2024. Access the poster here: https://zenodo.org/doi/10.5281/zenodo.10903886 Watch the video version of our poster here: https://youtu.be/N6ithJeCF4M