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.

本研究使用深度学习技术对多模态遥感数据进行融合，实现了在多米尼克飓风玛利亚后所获得的高分辨率正射影像和航空 LiDAR 数据中，对建筑物屋顶特征的自动分类。实验结果表明，多模态遥感数据的融合要优于任何单一数据源的应用，使得屋顶类型和屋顶材料的分类的 F1 得分分别达到了 0.93 和 0.92。本研究旨在帮助政府及时生成更准确的建筑信息，以提高加勒比地区的抗灾能力和灾害响应能力。