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Accurate and up-to-date information on building characteristics is essentialfor vulnerability assessment; however, the high costs and long timeframesassociated with conducting traditional field surveys can be an obstacle toobtaining critical exposure datasets needed for disaster risk management. Inthis work, we leverage deep learning techniques for the automatedclassification of roof characteristics from very high-resolution orthophotosand airborne LiDAR data obtained in Dominica following Hurricane Maria in 2017.We demonstrate that the fusion of multimodal earth observation data performsbetter than using any single data source alone. Using our proposed methods, weachieve F1 scores of 0.93 and 0.92 for roof type and roof materialclassification, respectively. This work is intended to help governments producemore timely building information to improve resilience and disaster response inthe Caribbean.

Fusing VHR Post-disaster Aerial Imagery and LiDAR Data for Roof Classification in the Caribbean