Deep learning based landslide density estimation on SAR data for rapid response

• URL: http://arxiv.org/abs/2211.10338v1
• Date: Fri, 18 Nov 2022 16:50:02 GMT
• Title: Deep learning based landslide density estimation on SAR data for rapid response
• Authors: Vanessa Boehm, Wei Ji Leong, Ragini Bal Mahesh, Ioannis Prapas, Edoardo Nemni, Freddie Kalaitzis, Siddha Ganju, Raul Ramos-Pollan
• Abstract summary: This work aims to produce landslide density estimates using Synthetic Aperture Radar (SAR) satellite imageries to prioritise emergency resources. We use the United States Geological Survey (USGS) Landslide Inventory data annotated by experts after Hurricane Mar'ia in Puerto Rico. Our method obtains 0.814 AUC in predicting the correct density estimation class at the chip level.
• Score: 0.8208704543835964
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work aims to produce landslide density estimates using Synthetic Aperture Radar (SAR) satellite imageries to prioritise emergency resources for rapid response. We use the United States Geological Survey (USGS) Landslide Inventory data annotated by experts after Hurricane Mar\'ia in Puerto Rico on Sept 20, 2017, and their subsequent susceptibility study which uses extensive additional information such as precipitation, soil moisture, geological terrain features, closeness to waterways and roads, etc. Since such data might not be available during other events or regions, we aimed to produce a landslide density map using only elevation and SAR data to be useful to decision-makers in rapid response scenarios. The USGS Landslide Inventory contains the coordinates of 71,431 landslide heads (not their full extent) and was obtained by manual inspection of aerial and satellite imagery. It is estimated that around 45\% of the landslides are smaller than a Sentinel-1 typical pixel which is 10m $\times$ 10m, although many are long and thin, probably leaving traces across several pixels. Our method obtains 0.814 AUC in predicting the correct density estimation class at the chip level (128$\times$128 pixels, at Sentinel-1 resolution) using only elevation data and up to three SAR acquisitions pre- and post-hurricane, thus enabling rapid assessment after a disaster. The USGS Susceptibility Study reports a 0.87 AUC, but it is measured at the landslide level and uses additional information sources (such as proximity to fluvial channels, roads, precipitation, etc.) which might not regularly be available in an rapid response emergency scenario.

Related papers

• Leveraging Citizen Science for Flood Extent Detection using Machine Learning Benchmark Dataset [0.9029386959445269]
  We create a labeled known water body extent and flooded area extents during known flooding events covering about 36,000 sq. kilometers of regions within mainland U.S and Bangladesh. We also leveraged citizen science by open-sourcing the dataset and hosting an open competition based on the dataset to rapidly prototype flood extent detection using community generated models. We believe the dataset adds to already existing datasets based on Sentinel-1C SAR data and leads to more robust modeling of flood extents.
  arXiv  Detail & Related papers  (2023-11-15T18:49:29Z)
• Transformer-based nowcasting of radar composites from satellite images for severe weather [45.0983299269404]
  We present a Transformer-based model for nowcasting ground-based radar image sequences using satellite data up to two hours lead time. Trained on a dataset reflecting severe weather conditions, the model predicts radar fields occurring under different weather phenomena. The model can support precipitation nowcasting across large domains without an explicit need for radar towers.
  arXiv  Detail & Related papers  (2023-10-30T13:17:38Z)
• Causality-informed Rapid Post-hurricane Building Damage Detection in Large Scale from InSAR Imagery [6.331801334141028]
  Timely and accurate assessment of hurricane-induced building damage is crucial for effective post-hurricane response and recovery efforts. Recently, remote sensing technologies provide large-scale optical or Interferometric Synthetic Aperture Radar (InSAR) imagery data immediately after a disastrous event. These InSAR imageries often contain highly noisy and mixed signals induced by co-occurring or co-located building damage, flood, flood/wind-induced vegetation changes, as well as anthropogenic activities. This paper introduces an approach for rapid post-hurricane building damage detection from InSAR imagery.
  arXiv  Detail & Related papers  (2023-10-02T18:56:05Z)
• Automating global landslide detection with heterogeneous ensemble deep-learning classification [44.99833362998488]
  Landslides threaten infrastructure, including roads, railways, buildings, and human life. Hazard-based spatial planning and early warning systems are cost-effective strategies to reduce the risk to society from landslides. Deep learning models have recently been applied for landside mapping using medium- to high-resolution satellite images as input.
  arXiv  Detail & Related papers  (2023-09-12T10:56:16Z)
• SAR-based landslide classification pretraining leads to better segmentation [0.8208704543835964]
  Rapid assessment after a natural disaster is key for prioritizing emergency resources. Deep learning algorithms can be applied to SAR data, but training them requires large labeled datasets. Here, we study how deep learning algorithms for landslide segmentation on SAR products can benefit from pretraining.
  arXiv  Detail & Related papers  (2022-11-17T22:57:18Z)
• Deep Learning for Rapid Landslide Detection using Synthetic Aperture Radar (SAR) Datacubes [0.8208704543835964]
  We provide pre-processed, machine-learning ready SAR datacubes for four globally located landslide events. We study the feasibility of SAR-based landslide detection with supervised deep learning (DL) DL models can be used to detect landslides from SAR data, achieving an Area under the Precision-Recall curve exceeding 0.7.
  arXiv  Detail & Related papers  (2022-11-05T10:31:57Z)
• Pangu-Weather: A 3D High-Resolution Model for Fast and Accurate Global Weather Forecast [91.9372563527801]
  We present Pangu-Weather, a deep learning based system for fast and accurate global weather forecast. For the first time, an AI-based method outperforms state-of-the-art numerical weather prediction (NWP) methods in terms of accuracy. Pangu-Weather supports a wide range of downstream forecast scenarios, including extreme weather forecast and large-member ensemble forecast in real-time.
  arXiv  Detail & Related papers  (2022-11-03T17:19:43Z)
• Embedding Earth: Self-supervised contrastive pre-training for dense land cover classification [61.44538721707377]
  We present Embedding Earth a self-supervised contrastive pre-training method for leveraging the large availability of satellite imagery. We observe significant improvements up to 25% absolute mIoU when pre-trained with our proposed method. We find that learnt features can generalize between disparate regions opening up the possibility of using the proposed pre-training scheme.
  arXiv  Detail & Related papers  (2022-03-11T16:14:14Z)
• Country-wide Retrieval of Forest Structure From Optical and SAR Satellite Imagery With Bayesian Deep Learning [74.94436509364554]
  We propose a Bayesian deep learning approach to densely estimate forest structure variables at country-scale with 10-meter resolution. Our method jointly transforms Sentinel-2 optical images and Sentinel-1 synthetic aperture radar images into maps of five different forest structure variables. We train and test our model on reference data from 41 airborne laser scanning missions across Norway.
  arXiv  Detail & Related papers  (2021-11-25T16:21:28Z)
• Real-time Outdoor Localization Using Radio Maps: A Deep Learning Approach [59.17191114000146]
  LocUNet: A convolutional, end-to-end trained neural network (NN) for the localization task. We show that LocUNet can localize users with state-of-the-art accuracy and enjoys high robustness to inaccuracies in the estimations of radio maps.
  arXiv  Detail & Related papers  (2021-06-23T17:27:04Z)
• Improving Landslide Detection on SAR Data through Deep Learning [0.0]
  We use deep-learning convolution neural networks (CNNs) to assess the landslide mapping and classification performances on optical images. We analyzed the conditions before and after an earthquake that triggered about 8000 coseismic landslides. CNNs based on the combination of ground range detected (GRD) SAR data reached overall accuracies beyond 94%.
  arXiv  Detail & Related papers  (2021-05-03T12:37:57Z)

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.