Global Mapping of Exposure and Physical Vulnerability Dynamics in Least Developed Countries using Remote Sensing and Machine Learning
Joshua Dimasaka, Christian Geiß, Emily So
TL;DR
The paper tackles the lack of interoperable exposure and physical vulnerability data in least developed countries for disaster risk reduction under SFDRR. It introduces OpenSendaiBench, a public 60-GB benchmark for 47 countries combining METEOR-derived exposure with time-series Sentinel-1 SAR and Sentinel-2 MSI imagery. A ResNet-50 CNN with a probabilistic target via $P_{nonexceedance}$ is used to map informal constructions; the transformation treats building counts as $Y \sim \ln \mathcal{N}(\mu,\sigma^2)$. Initial results from Dhaka show SAR-based inputs yield lower error than RGB, demonstrating feasibility for large-scale risk quantification and informing SFDRR-related planning.
Abstract
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.