Mapping Housing Stock Characteristics from Drone Images for Climate Resilience in the Caribbean

Abstract

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 footprints and roof classification maps. By strengthening local capacity within government agencies to leverage AI and Earth Observation-based solutions, this work seeks to improve the climate resilience of the housing sector in small island developing states in the Caribbean.