Mapping Housing Stock Characteristics from Drone Images for Climate
  Resilience in the Caribbean
Isabelle Tingzon Nuala Margaret Cowan Pierre Chrzanowski
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.