Version 1
: Received: 10 April 2024 / Approved: 10 April 2024 / Online: 11 April 2024 (04:12:10 CEST)
How to cite:
Peters, S.; Liu, J.; Keppel, G.; Wendleder, A.; Xu, P. Detecting Co-Seismic Landslides in GEE using Machine-Learning Algorithms on combined Multispectral and RADAR Imagery. Preprints2024, 2024040724. https://doi.org/10.20944/preprints202404.0724.v1
Peters, S.; Liu, J.; Keppel, G.; Wendleder, A.; Xu, P. Detecting Co-Seismic Landslides in GEE using Machine-Learning Algorithms on combined Multispectral and RADAR Imagery. Preprints 2024, 2024040724. https://doi.org/10.20944/preprints202404.0724.v1
Peters, S.; Liu, J.; Keppel, G.; Wendleder, A.; Xu, P. Detecting Co-Seismic Landslides in GEE using Machine-Learning Algorithms on combined Multispectral and RADAR Imagery. Preprints2024, 2024040724. https://doi.org/10.20944/preprints202404.0724.v1
APA Style
Peters, S., Liu, J., Keppel, G., Wendleder, A., & Xu, P. (2024). Detecting Co-Seismic Landslides in GEE using Machine-Learning Algorithms on combined Multispectral and RADAR Imagery. Preprints. https://doi.org/10.20944/preprints202404.0724.v1
Chicago/Turabian Style
Peters, S., Anna Wendleder and Peiliang Xu. 2024 "Detecting Co-Seismic Landslides in GEE using Machine-Learning Algorithms on combined Multispectral and RADAR Imagery" Preprints. https://doi.org/10.20944/preprints202404.0724.v1
Abstract
Landslides, resulting from disturbances in slope equilibrium, pose a significant threat to landscapes, infrastructure, and human life. Triggered by factors such as intense precipitation, seismic activities, or volcanic eruptions, these events can cause extensive damage and endanger nearby communities. A comprehensive understanding of landslide characteristics, including spatio-temporal patterns, dimensions, and morphology, is vital for effective landslide-disaster management. Existing Remote Sensing approaches mostly use either optical or Synthetic Aperture Radar (SAR) sensors. Integrating information from both these types of sensors promises greater accuracy for identifying and locating landslides.
This study proposes a novel approach, the ML-LaDeCORsat (Machine Learning-based coseismic Landslide Detection using Combined Optical and Radar Satellite Imagery), that integrates freely available Sentinel-1, Palsar-2, and Sentinel-2 imagery data, along with relevant spectral indices and suitable bands using machine-learning-based classification for coseismic landslide detection implemented in Google Earth Engine (GEE). The approach includes a robust and reproducible training and validation strategy. Using landslides from four different earthquake case studies, we demonstrate the superiority of our approach over existing solutions in coseismic landslide iden-tification and localization, providing a detection accuracy of 87-92%. ML-LaDeCORsat can be adapted to other landslide events (GEE script is provided). Transfer learning experiments proofed that our model can be applied to other coseismic landslide events without the need for additional training data. Our novel approach therefore facilitates quick and reliable identification of co-seismic landslides, highlighting its potential to contribute towards more effective disaster management.
Keywords
landslide detection; satellite remote sensing; machine learning; classification; Google Earth Engine; transfer learning
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
