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      A comprehensive transferability evaluation of U-Net and ResU-Net for landslide detection from Sentinel-2 data (case study areas from Taiwan, China, and Japan)

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          Abstract

          Earthquakes and heavy rainfalls are the two leading causes of landslides around the world. Since they often occur across large areas, landslide detection requires rapid and reliable automatic detection approaches. Currently, deep learning (DL) approaches, especially different convolutional neural network and fully convolutional network (FCN) algorithms, are reliably achieving cutting-edge accuracies in automatic landslide detection. However, these successful applications of various DL approaches have thus far been based on very high resolution satellite images (e.g., GeoEye and WorldView), making it easier to achieve such high detection performances. In this study, we use freely available Sentinel-2 data and ALOS digital elevation model to investigate the application of two well-known FCN algorithms, namely the U-Net and residual U-Net (or so-called ResU-Net), for landslide detection. To our knowledge, this is the first application of FCN for landslide detection only from freely available data. We adapt the algorithms to the specific aim of landslide detection, then train and test with data from three different case study areas located in Western Taitung County (Taiwan), Shuzheng Valley (China), and Eastern Iburi (Japan). We characterize three different window size sample patches to train the algorithms. Our results also contain a comprehensive transferability assessment achieved through different training and testing scenarios in the three case studies. The highest f1-score value of 73.32% was obtained by ResU-Net, trained with a dataset from Japan, and tested on China’s holdout testing area using the sample patch size of 64 × 64 pixels.

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          Most cited references48

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          Object based image analysis for remote sensing

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            Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources

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              Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services

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                Author and article information

                Contributors
                omid.ghorbanzadeh@stud.sbg.ac.at
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                16 July 2021
                16 July 2021
                2021
                : 11
                : 14629
                Affiliations
                [1 ]GRID grid.7039.d, ISNI 0000000110156330, Department of Geoinformatics—Z_GIS, , University of Salzburg, ; 5020 Salzburg, Austria
                [2 ]GRID grid.461897.5, Machine Learning Group, , Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, ; Chemnitzer Str. 40, 09599 Freiberg, Germany
                [3 ]GRID grid.510961.a, Institute of Advanced Research in Artificial Intelligence (IARAI), ; Landstraβer Hauptstraβe 5, 1030 Vienna, Austria
                [4 ]GRID grid.419303.c, ISNI 0000 0001 2180 9405, Institute of Geography, , Slovak Academy of Sciences, ; Stefanikova 49, 814 73 Bratislava, Slovakia
                Article
                94190
                10.1038/s41598-021-94190-9
                8285525
                34272463
                ec342915-e1ea-461c-af8e-e1458e5706dc
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 22 February 2021
                : 23 June 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100002428, Austrian Science Fund;
                Award ID: DK W 1237-N23
                Award ID: DK W 1237-N23
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                natural hazards,hydrogeology
                Uncategorized
                natural hazards, hydrogeology

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