Mapping Housing Stock Characteristics from Drone Images for Climate Resilience in the Caribbean (Papers Track)

Isabelle Tingzon (The World Bank); Nuala Margaret Cowan (The World Bank); Pierre Chrzanowski (The World Bank)

Paper PDF Poster File NeurIPS 2023 Poster Cite
Disaster Management and Relief


Comprehensive information on housing stock is crucial for climate adaptation initiatives aiming to reduce the adverse impacts of climate-extreme hazards in high-risk regions like the Caribbean. In this study, we propose a workflow for rapidly generating critical baseline housing stock data using very high-resolution drone images and deep learning techniques. Specifically, our work leverages the Segment Anything Model and convolutional neural networks for the automated generation of building footprint and roof classification maps. By enhancing local capacity in government agencies, this work seeks to improve the climate resilience of the housing sector in small island developing states in the Caribbean.