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。

基于 CNN 的卫星遥感图像和 LiDAR 数据融合在加勒比地区的屋顶分类

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

City models and height maps of urban areas serve as a valuable data source
for numerous applications, such as disaster management or city planning. While
this information is not globally available, it can be substituted by digital
surface models (DSMs), automatically produced from inexpensive satellite
imagery. However, stereo DSMs often suffer from noise and blur. Furthermore,
they are heavily distorted by vegetation, which is of lesser relevance for most
applications. Such basic models can be filtered by convolutional neural
networks (CNNs), trained on labels derived from digital elevation models (DEMs)
and 3D city models, in order to obtain a refined DSM. We propose a modular
multi-task learning concept that consolidates existing approaches into a
generalized framework. Our encoder-decoder models with shared encoders and
multiple task-specific decoders leverage roof type classification as a
secondary task and multiple objectives including a conditional adversarial
term. The contributing single-objective losses are automatically weighted in
the final multi-task loss function based on learned uncertainty estimates. We
evaluated the performance of specific instances of this family of network
architectures. Our method consistently outperforms the state of the art on
common data, both quantitatively and qualitatively, and generalizes well to a
new dataset of an independent study area.

该研究提出了一种基于数字表面模型和卷积神经网络的多任务学习框架，其中后者利用屋顶类型分类作为次要任务和多个目标，以获得优化的表面模型。此方法在常见数据上始终优于其他方法，并且对一个独立研究区域的新数据具有很好的泛化能力。