Fusing VHR Post-disaster Aerial Imagery and LiDAR Data for Roof Classification in the Caribbean
This work helps governments in the Caribbean produce timely building data to improve resilience and disaster response, though it is incremental as it applies existing fusion techniques to a specific domain.
The paper tackled the problem of obtaining accurate building information for disaster risk management by developing a deep learning method to classify roof characteristics from aerial imagery and LiDAR data after Hurricane Maria, achieving F1 scores of 0.93 for roof type and 0.92 for roof material classification.
Accurate and up-to-date information on building characteristics is essential for vulnerability assessment; however, the high costs and long timeframes associated with conducting traditional field surveys can be an obstacle to obtaining critical exposure datasets needed for disaster risk management. In this work, we leverage deep learning techniques for the automated classification of roof characteristics from very high-resolution orthophotos and airborne LiDAR data obtained in Dominica following Hurricane Maria in 2017. We demonstrate that the fusion of multimodal earth observation data performs better than using any single data source alone. Using our proposed methods, we achieve F1 scores of 0.93 and 0.92 for roof type and roof material classification, respectively. This work is intended to help governments produce more timely building information to improve resilience and disaster response in the Caribbean.