4.7 Article

Machine learning powered high-resolution co-seismic landslide detection

Journal

GONDWANA RESEARCH
Volume 123, Issue -, Pages 217-237

Publisher

ELSEVIER
DOI: 10.1016/j.gr.2022.07.004

Keywords

Machine learning; Earthquake; Landslides; Artificial intelligence; Synthetic aperture radar; Risk management

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This paper presents an integrated machine learning method for co-seismic landslide detection, combining multi-source data, pixel-based and object-based treatments, and ML techniques. Two case studies in China demonstrate the outstanding performance and generic nature of the proposed method in high-resolution co-seismic landslide detection.
Numerous co-seismic landslides can be triggered by a strong earthquake. Fast and accurate detection and mapping of these landslides are crucial for rapid risk assessment and humanitarian assistance. Traditional visual interpretation is time-consuming and heavily influenced by human judgement. This paper pre-sents an integrated machine learning (ML) powered method for co-seismic landslide detection, which combines multi-source data, pixel-based and object-based treatments, and ML techniques. The proposed method first fuses multi-source data, including optical images, synthetic aperture radar images and dig-ital elevation models, to produce data layers and reference landslide inventories for learning. After that, ML algorithms are coupled with pixel-based and object-based treatments to produce a series of co-seismic landslide detection models. Powerful models are subsequently recommended after comprehen-sive evaluation. Two case studies of earthquakes in China are presented. The first is in the epicenter area of the 2008 Wenchuan earthquake. Even trained with only 5% of local data, the proposed method still achieves an accuracy of area (AOA) of 97.77% in the trained area and an AOA of 93.17% in a new area. If trained with the entire local data, an AOA of 99.99% in the trained area and 94.56% in a new area can be obtained. The second case detects the co-seismic landslides induced by the 2017 Jiuzhaigou earth-quake. An AOA of 96.55% is achieved with 5% of local data. The case studies confirm the outstanding per-formance and generic nature of the proposed machine learning method, greatly advancing the state of the art of high-resolution co-seismic landslide detection and the landslide science.(c) 2022 International Association for Gondwana Research. Published by Elsevier B.V. All rights reserved.

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