We propose a novel approach for rapid segmentation of flooded buildings by
fusing multiresolution, multisensor, and multitemporal satellite imagery in a
convolutional neural network. Our model significantly expedites the generation
of satellite imagery-based flood maps, crucial for first responders and local
authorities in the early stages of flood events. By incorporating multitemporal
satellite imagery, our model allows for rapid and accurate post-disaster damage
assessment and can be used by governments to better coordinate medium- and
long-term financial assistance programs for affected areas. The network
consists of multiple streams of encoder-decoder architectures that extract
spatiotemporal information from medium-resolution images and spatial
information from high-resolution images before fusing the resulting
representations into a single medium-resolution segmentation map of flooded
buildings. We compare our model to state-of-the-art methods for building
footprint segmentation as well as to alternative fusion approaches for the
segmentation of flooded buildings and find that our model performs best on both
tasks. We also demonstrate that our model produces highly accurate segmentation
maps of flooded buildings using only publicly available medium-resolution data
instead of significantly more detailed but sparsely available very
high-resolution data. We release the first open-source dataset of fully
preprocessed and labeled multiresolution, multispectral, and multitemporal
satellite images of disaster sites along with our source code.

本研究提出了一个基于卷积神经网络可快速分割洪水淹没建筑的新方法，并结合多分辨率、多传感器、多时相卫星图像，本方法迅速生成卫星图像洪水地图，有助于应对洪水事件的早期响应；同时，通过结合多时相信息，本方法也能用于快速而精确的灾后伤害评估，并可帮助政府更好地协调中长期的财政援助计划。我们还将我们基于编码器 - 解码器结构的多流视频数据融合方法与其他现有研究进行了比较，并证明了它的性能更佳。此外，我们还发布了一个全面预处理和标记的多分辨率和多时相卫星图像灾害数据集以及我们的源代码。