Breaking Limits of Remote Sensing by Deep Learning From Simulated Data for Flood and Debris-Flow Mapping
Breaking Limits of Remote Sensing by Deep Learning From Simulated Data for Flood and Debris-Flow Mapping
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DOI：
10.1109/tgrs.2020.3035469
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发表时间：
2022
影响因子：
8.2
通讯作者：
N. Yokoya;Kazuki Yamanoi;Wei He;Gerald Baier;B. Adriano;H. Miura;S. Oishi
中科院分区：
文献类型：
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作者：
N. Yokoya;Kazuki Yamanoi;Wei He;Gerald Baier;B. Adriano;H. Miura;S. Oishi
结合深度学习和数值模拟，我们提出了一个从遥感图像中估计淹没深度(最大水位)和泥石流引起的地形变形的框架。水和泥石流模拟器为各种人为灾难情景生成训练数据。我们发现，基于基于注意U网和LinkNet架构的回归模型可以从遥感获取的变化检测地图和数字高程模型中预测最大水位和地形变形。提出的框架具有修复能力，从而减少了遥感图像分析中不可避免的假阴性。我们的框架打破了遥感的限制，能够快速估计淹没深度和地形变形，这是紧急响应的基本信息，包括救援和救济活动。我们用合成数据和真实数据对同时引发洪水和泥石流的两个灾害事件进行了实验，并从定量和定性两个方面证明了我们方法的有效性。我们的代码和数据集可在https://github.com/nyokoya/dlsim.上获得
We propose a framework that estimates the inundation depth (maximum water level) and debris-flow-induced topographic deformation from remote sensing imagery by integrating deep learning and numerical simulation. A water and debris-flow simulator generates training data for various artificial disaster scenarios. We show that regression models based on Attention U-Net and LinkNet architectures trained on such synthetic data can predict the maximum water level and topographic deformation from a remote sensing-derived change detection map and a digital elevation model. The proposed framework has an inpainting capability, thus mitigating the false negatives that are inevitable in remote sensing image analysis. Our framework breaks limits of remote sensing and enables rapid estimation of inundation depth and topographic deformation, essential information for emergency response, including rescue and relief activities. We conduct experiments with both synthetic and real data for two disaster events that caused simultaneous flooding and debris flows and demonstrate the effectiveness of our approach quantitatively and qualitatively. Our code and data sets are available at https://github.com/nyokoya/dlsim.