  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.
