Multi‐Annual Inventorying of Retrogressive Thaw Slumps Using Domain Adaptation
Abstract Retrogressive Thaw Slumps (RTSs), a form of thermokarst hazards, pose risks to hydrological and ecological environments and the safety of the Qinghai‐Tibet Engineering Corridor. We still lack the knowledge about the geographic locations of RTSs and their dynamically changing spatial margins...
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| Format: | Article |
| Language: | English |
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Wiley
2025-03-01
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| Series: | Journal of Geophysical Research: Machine Learning and Computation |
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| Online Access: | https://doi.org/10.1029/2024JH000370 |
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| author | Yiling Lin Xie Hu Haoyu Lu Fujun Niu Gengnian Liu Lingcao Huang Shanghang Zhang Jifu Liu Yunhuai Liu |
| author_facet | Yiling Lin Xie Hu Haoyu Lu Fujun Niu Gengnian Liu Lingcao Huang Shanghang Zhang Jifu Liu Yunhuai Liu |
| author_sort | Yiling Lin |
| collection | DOAJ |
| description | Abstract Retrogressive Thaw Slumps (RTSs), a form of thermokarst hazards, pose risks to hydrological and ecological environments and the safety of the Qinghai‐Tibet Engineering Corridor. We still lack the knowledge about the geographic locations of RTSs and their dynamically changing spatial margins. However, visual interpretation is labor‐intensive while the present‐day deep learning methods become ineffective when the model trained in one year is directly transferred to another. To enhance the model's generalization ability, here we implemented and compared three domain adaptation methods, that is, the classic supervised fine‐tuning method and two proposed unsupervised methods: Image StyleTransfer Domain Adaptation (ISTDA) and the Tversky Adversarial Domain Adaptation (TADA) network. In our proposed ISTDA, we uniformed the contextual information of multi‐temporal images by Cycle Generative Adversarial Network (CycleGAN). We introduced the Tversky loss and the automatic adjustment of weights for multiple loss functions to suppress false positives and to improve the generalization of TADA. We tested three methods' performance in Beiluhe region over the Qinghai‐Tibet Plateau using PlanetScope optical images during 2019–2022. The three domain adaptation methods are successful in generating regional, multi‐annual RTS inventories. Remarkably, TADA sustains good performance in complex transfer scenarios without additional label cost, achieving an F1 increase of 14.32%–24.17% compared to classic methods. Our work is the first to apply an unsupervised domain adaptation to automatically map the RTSs on a multi‐annual timescale, demonstrating a strong potential of its applicability for monitoring large‐scale, multi‐temporal evolution of geomorphological features. |
| format | Article |
| id | doaj-art-ec1fc405f66b4909b9b45eebd88e10c3 |
| institution | DOAJ |
| issn | 2993-5210 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Geophysical Research: Machine Learning and Computation |
| spelling | doaj-art-ec1fc405f66b4909b9b45eebd88e10c32025-08-20T02:49:36ZengWileyJournal of Geophysical Research: Machine Learning and Computation2993-52102025-03-0121n/an/a10.1029/2024JH000370Multi‐Annual Inventorying of Retrogressive Thaw Slumps Using Domain AdaptationYiling Lin0Xie Hu1Haoyu Lu2Fujun Niu3Gengnian Liu4Lingcao Huang5Shanghang Zhang6Jifu Liu7Yunhuai Liu8College of Urban and Environmental Sciences Peking University Beijing ChinaCollege of Urban and Environmental Sciences Peking University Beijing ChinaSchool of Artificial Intelligence Beijing Normal University Beijing ChinaCivil Engineering at the State Key Laboratory of Frozen Soil Engineering Northwest Institute of Eco‐environment and Resources Chinese Academy of Sciences Gansu ChinaCollege of Urban and Environmental Sciences Peking University Beijing ChinaInstitute of Space and Earth Information Science The Chinese University of Hong Kong Hong Kong ChinaThe School of Computer Science Peking University Beijing ChinaFaculty of Geographical Science Beijing Normal University Beijing ChinaThe School of Computer Science Peking University Beijing ChinaAbstract Retrogressive Thaw Slumps (RTSs), a form of thermokarst hazards, pose risks to hydrological and ecological environments and the safety of the Qinghai‐Tibet Engineering Corridor. We still lack the knowledge about the geographic locations of RTSs and their dynamically changing spatial margins. However, visual interpretation is labor‐intensive while the present‐day deep learning methods become ineffective when the model trained in one year is directly transferred to another. To enhance the model's generalization ability, here we implemented and compared three domain adaptation methods, that is, the classic supervised fine‐tuning method and two proposed unsupervised methods: Image StyleTransfer Domain Adaptation (ISTDA) and the Tversky Adversarial Domain Adaptation (TADA) network. In our proposed ISTDA, we uniformed the contextual information of multi‐temporal images by Cycle Generative Adversarial Network (CycleGAN). We introduced the Tversky loss and the automatic adjustment of weights for multiple loss functions to suppress false positives and to improve the generalization of TADA. We tested three methods' performance in Beiluhe region over the Qinghai‐Tibet Plateau using PlanetScope optical images during 2019–2022. The three domain adaptation methods are successful in generating regional, multi‐annual RTS inventories. Remarkably, TADA sustains good performance in complex transfer scenarios without additional label cost, achieving an F1 increase of 14.32%–24.17% compared to classic methods. Our work is the first to apply an unsupervised domain adaptation to automatically map the RTSs on a multi‐annual timescale, demonstrating a strong potential of its applicability for monitoring large‐scale, multi‐temporal evolution of geomorphological features.https://doi.org/10.1029/2024JH000370retrogressive thaw slumps (RTSs)domain adaptationtemporal generalizationsemantic segmentationQinghai‐Tibet plateau |
| spellingShingle | Yiling Lin Xie Hu Haoyu Lu Fujun Niu Gengnian Liu Lingcao Huang Shanghang Zhang Jifu Liu Yunhuai Liu Multi‐Annual Inventorying of Retrogressive Thaw Slumps Using Domain Adaptation Journal of Geophysical Research: Machine Learning and Computation retrogressive thaw slumps (RTSs) domain adaptation temporal generalization semantic segmentation Qinghai‐Tibet plateau |
| title | Multi‐Annual Inventorying of Retrogressive Thaw Slumps Using Domain Adaptation |
| title_full | Multi‐Annual Inventorying of Retrogressive Thaw Slumps Using Domain Adaptation |
| title_fullStr | Multi‐Annual Inventorying of Retrogressive Thaw Slumps Using Domain Adaptation |
| title_full_unstemmed | Multi‐Annual Inventorying of Retrogressive Thaw Slumps Using Domain Adaptation |
| title_short | Multi‐Annual Inventorying of Retrogressive Thaw Slumps Using Domain Adaptation |
| title_sort | multi annual inventorying of retrogressive thaw slumps using domain adaptation |
| topic | retrogressive thaw slumps (RTSs) domain adaptation temporal generalization semantic segmentation Qinghai‐Tibet plateau |
| url | https://doi.org/10.1029/2024JH000370 |
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