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landslide-sar-unet

Repository for the paper "Deep Learning for Rapid Landslide Detection using Synthetic Aperture Radar (SAR) Datacubes"

https://github.com/iprapas/landslide-sar-unet

Science Score: 67.0%

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    CITATION.cff file
    Found CITATION.cff file
  • ✓
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    Found codemeta.json file
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    .zenodo.json file
    Found .zenodo.json file
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    DOI references
    Found 5 DOI reference(s) in README
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    Academic publication links
    Links to: arxiv.org, zenodo.org
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    JOSS paper metadata
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    Scientific vocabulary similarity
    Low similarity (9.0%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

Repository for the paper "Deep Learning for Rapid Landslide Detection using Synthetic Aperture Radar (SAR) Datacubes"

Basic Info
  • Host: GitHub
  • Owner: iprapas
  • License: mit
  • Language: Python
  • Default Branch: main
  • Size: 18.6 KB
Statistics
  • Stars: 37
  • Watchers: 3
  • Forks: 4
  • Open Issues: 1
  • Releases: 0
Created over 3 years ago · Last pushed over 1 year ago
Metadata Files
Readme License Citation

README.md

Deep Learning for Rapid Landslide Detection using Synthetic Aperture Radar (SAR) Datacubes

Repository for the paper Deep Learning for Rapid Landslide Detection using Synthetic Aperture Radar (SAR) Datacubes

Installing the requirements

To run the experiments presented in the paper make sure to install the requirements.

pip install -r requirements.txt

Downloading the data

Download the data from Zenodo. Particularly, the hokkaido datacube is needed.

Running the experiments

To reproduce the experiments from the paper run the script

bash scripts/run_experiments.sh

IMPORTANT: After, decompressing the downloaded hokkaido cube, make sure to add datacube path to the script before running it.

Notes

The experiments have run on an NVIDIA V100 GPU in Google Cloud.

Citation

If you use this code for your research, please cite our paper:

``` @misc{https://doi.org/10.48550/arxiv.2211.02869, doi = {10.48550/ARXIV.2211.02869},

url = {https://arxiv.org/abs/2211.02869},

author = {Boehm, Vanessa and Leong, Wei Ji and Mahesh, Ragini Bal and Prapas, Ioannis and Nemni, Edoardo and Kalaitzis, Freddie and Ganju, Siddha and Ramos-Pollan, Raul},

keywords = {Signal Processing (eess.SP), Computer Vision and Pattern Recognition (cs.CV), Image and Video Processing (eess.IV), FOS: Electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Computer and information sciences, FOS: Computer and information sciences},

title = {Deep Learning for Rapid Landslide Detection using Synthetic Aperture Radar (SAR) Datacubes},

publisher = {arXiv},

year = {2022},

copyright = {Creative Commons Attribution 4.0 International} } ```

Acknowledgements

This work has been enabled by the Frontier Development Lab Program (FDL). FDL is a collaboration between SETI Institute and Trillium Technologies Inc., in partnership with the Department of Energy (DOE), National Aeronautics and Space Administration (NASA), the U.S. Geological Survey (USGS), Google Cloud and NVIDIA. The material is based upon work supported by NASA under award No(s) NNX14AT27A.

Owner

  • Login: iprapas
  • Kind: user

Citation (CITATION.cff) 

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: >-
  Deep Learning for Rapid Landslide Detection using
  Synthetic Aperture Radar (SAR) Datacubes
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
repository-code: "https://github.com/iprapas/landslide-sar-unet"
authors:
  - given-names: Vanessa
    family-names: Boehm
    affiliation: >-
      University of California Berkeley, United
      States
    orcid: 'https://orcid.org/0000-0003-3801-1912'
  - given-names: Wei Ji
    family-names: Leong
    affiliation: 'The Ohio State University, United States'
    orcid: 'https://orcid.org/0000-0003-2354-1988'
  - given-names: Ragini Bal
    family-names: Mahesh
    affiliation: 'German Aerospace Center DLR, Germany'
    orcid: 'https://orcid.org/0000-0002-2747-9811'
  - given-names: Ioannis
    family-names: Prapas
    affiliation: 'University of Valencia, Spain'
    orcid: 'https://orcid.org/0000-0002-9111-4112'
  - given-names: Edoardo
    family-names: Nemni
    affiliation: 'United Nations Satellite Centre, Switzerland'
    orcid: 'https://orcid.org/0000-0002-0166-4613'
  - family-names: Kalaitzis
    given-names: Freddie
    affiliation: 'University of Oxford, United Kingdom'
    orcid: 'https://orcid.org/0000-0002-1471-646X'
  - given-names: Siddha
    family-names: Ganju
    affiliation: 'NVIDIA, United States'
    orcid: 'https://orcid.org/0000-0002-9462-4898'
  - given-names: Raul
    family-names: Ramos-Pollan
    affiliation: 'Universidad de Antioquia, Colombia'
    orcid: 'https://orcid.org/0000-0001-6195-3612'
preferred-citation:
  type: conference-paper
  authors:
  - given-names: Vanessa
    family-names: Boehm
    affiliation: >-
      University of California Berkeley, United
      States
    orcid: 'https://orcid.org/0000-0003-3801-1912'
  - given-names: Wei Ji
    family-names: Leong
    affiliation: 'The Ohio State University, United States'
    orcid: 'https://orcid.org/0000-0003-2354-1988'
  - given-names: Ragini Bal
    family-names: Mahesh
    affiliation: 'German Aerospace Center DLR, Germany'
    orcid: 'https://orcid.org/0000-0002-2747-9811'
  - given-names: Ioannis
    family-names: Prapas
    affiliation: 'University of Valencia, Spain'
    orcid: 'https://orcid.org/0000-0002-9111-4112'
  - given-names: Edoardo
    family-names: Nemni
    affiliation: 'United Nations Satellite Centre, Switzerland'
    orcid: 'https://orcid.org/0000-0002-0166-4613'
  - family-names: Kalaitzis
    given-names: Freddie
    affiliation: 'University of Oxford, United Kingdom'
    orcid: 'https://orcid.org/0000-0002-1471-646X'
  - given-names: Siddha
    family-names: Ganju
    affiliation: 'NVIDIA, United States'
    orcid: 'https://orcid.org/0000-0002-9462-4898'
  - given-names: Raul
    family-names: Ramos-Pollan
    affiliation: 'Universidad de Antioquia, Colombia'
    orcid: 'https://orcid.org/0000-0001-6195-3612'
  doi: "10.48550/arXiv.2211.02869"
  conference:
    name: "NeurIPS 2022 workshop on Tackling Climate Change with Machine Learning"
    date-end: "2022-12-09"
  title: "Deep Learning for Rapid Landslide Detection using Synthetic Aperture Radar (SAR) Datacubes"
  year: 2022

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