Breaking the Limits of Remote Sensing by Simulation and Deep Learning for Flood and Debris Flow Mapping

• URL: http://arxiv.org/abs/2006.05180v1
• Date: Tue, 9 Jun 2020 10:59:15 GMT
• Title: Breaking the Limits of Remote Sensing by Simulation and Deep Learning for Flood and Debris Flow Mapping
• Authors: Naoto Yokoya, Kazuki Yamanoi, Wei He, Gerald Baier, Bruno Adriano, Hiroyuki Miura, Satoru Oishi
• Abstract summary: We propose a framework that estimates inundation depth and debris-flow-induced topographic deformation from remote sensing imagery. 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.
• Score: 13.167695669500391
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a framework that estimates 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 the 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.

Related papers

• Machine learned reconstruction of tsunami dynamics from sparse observations [0.0]
  We use a transformer neural network designed for sparse sensing applications to estimate full-field surface height measurements of tsunami waves. We train the model on a dataset consisting of 8 tsunami simulations whose epicenters correspond to historical USGS earthquake records. The results show remarkable resolution of fine scale phase and amplitude features from the true field, provided that at least a few of the sensors have obtained a non-zero signal.
  arXiv  Detail & Related papers  (2024-11-20T00:42:40Z)
• Deep Vision-Based Framework for Coastal Flood Prediction Under Climate Change Impacts and Shoreline Adaptations [0.3413711585591077]
  We present a systematic framework for training high-fidelity Deep Vision-based coastal flood prediction models in low-data settings. We also introduce a deep CNN architecture tailored specifically to the coastal flood prediction problem at hand. The performance of the developed DL models is validated against commonly adopted geostatistical regression methods.
  arXiv  Detail & Related papers  (2024-06-06T19:54:34Z)
• Multi-Modal Learning-based Reconstruction of High-Resolution Spatial Wind Speed Fields [46.72819846541652]
  We propose a framework based on Vari Data Assimilation and Deep Learning concepts. This framework is applied to recover rich-in-time, high-resolution information on sea surface wind speed.
  arXiv  Detail & Related papers  (2023-12-14T13:40:39Z)
• An evaluation of deep learning models for predicting water depth evolution in urban floods [59.31940764426359]
  We compare different deep learning models for prediction of water depth at high spatial resolution. Deep learning models are trained to reproduce the data simulated by the CADDIES cellular-automata flood model. Our results show that the deep learning models present in general lower errors compared to the other methods.
  arXiv  Detail & Related papers  (2023-02-20T16:08:54Z)
• Learning to Simulate Realistic LiDARs [66.7519667383175]
  We introduce a pipeline for data-driven simulation of a realistic LiDAR sensor. We show that our model can learn to encode realistic effects such as dropped points on transparent surfaces. We use our technique to learn models of two distinct LiDAR sensors and use them to improve simulated LiDAR data accordingly.
  arXiv  Detail & Related papers  (2022-09-22T13:12:54Z)
• SimuShips -- A High Resolution Simulation Dataset for Ship Detection with Precise Annotations [0.0]
  State-of-the-art obstacle detection algorithms are based on convolutional neural networks (CNNs) SimuShips is a publicly available simulation-based dataset for maritime environments.
  arXiv  Detail & Related papers  (2022-09-22T07:33:31Z)
• Unsupervised Restoration of Weather-affected Images using Deep Gaussian Process-based CycleGAN [92.15895515035795]
  We describe an approach for supervising deep networks that are based on CycleGAN. We introduce new losses for training CycleGAN that lead to more effective training, resulting in high-quality reconstructions. We demonstrate that the proposed method can be effectively applied to different restoration tasks like de-raining, de-hazing and de-snowing.
  arXiv  Detail & Related papers  (2022-04-23T01:30:47Z)
• Interpretability in Convolutional Neural Networks for Building Damage Classification in Satellite Imagery [0.0]
  We use a dataset that includes labeled pre- and post-disaster satellite imagery to assess building damage on a per-building basis. We train multiple convolutional neural networks (CNNs) to assess building damage on a per-building basis. Our research seeks to computationally contribute to aiding in this ongoing and growing humanitarian crisis, heightened by anthropogenic climate change.
  arXiv  Detail & Related papers  (2022-01-24T16:55:56Z)
• Lidar Light Scattering Augmentation (LISA): Physics-based Simulation of Adverse Weather Conditions for 3D Object Detection [60.89616629421904]
  Lidar-based object detectors are critical parts of the 3D perception pipeline in autonomous navigation systems such as self-driving cars. They are sensitive to adverse weather conditions such as rain, snow and fog due to reduced signal-to-noise ratio (SNR) and signal-to-background ratio (SBR)
  arXiv  Detail & Related papers  (2021-07-14T21:10:47Z)
• Generating Physically-Consistent Satellite Imagery for Climate Visualizations [53.61991820941501]
  We train a generative adversarial network to create synthetic satellite imagery of future flooding and reforestation events. A pure deep learning-based model can generate flood visualizations but hallucinates floods at locations that were not susceptible to flooding. We publish our code and dataset for segmentation guided image-to-image translation in Earth observation.
  arXiv  Detail & Related papers  (2021-04-10T15:00:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.