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Nevertheless, current InSAR deformation forecasting methods employing deep learning strategies such as the traditional long short-term memory (LSTM) and recent transformer models encounter difficulties in effectively capturing temporal features. Moreover, they are limited in their ability to directly integrate spatial information. In this paper, an innovative deep learning approach named Spacetimeformer is proposed for predicting medium- and short-term InSAR deformation of RTSs in the Chumar River area. This method employs a transformer architecture with a spatiotemporal attention mechanism, which enhances the long-term prediction capabilities of time series models and dynamic spatial modeling. It is applicable to multivariate InSAR spatiotemporal deformation prediction problems. The findings include a list of 72 RTSs compiled based on derived InSAR deformation maps and Sentinel-2 optical images, of which 64 have an average deformation rate exceeding 10 mm\/year, indicating signs of permafrost degradation. The density distribution of the displacement maps predicted by the Spacetimeformer model aligned well with the InSAR deformation maps obtained from the small baseline subset (SBAS) method, with the overall prediction deviation controlled within 20 mm. In addition, the point-scale prediction results were compared with LSTM and transformer models. This study indicates that the Spacetimeformer network achieved good results in predicting the deformation of RTSs, with a root mean square error of 1.249 mm. The Spacetimeformer method for deformation prediction with the spacetime mechanism presented in this study can serve as a general framework for multivariate deformation prediction based on InSAR results. It can also quantitatively assess the spatial deformation characteristics and deformation trends of RTSs.<\/jats:p>","DOI":"10.3390\/rs16111891","type":"journal-article","created":{"date-parts":[[2024,5,24]],"date-time":"2024-05-24T11:17:52Z","timestamp":1716549472000},"page":"1891","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Spatiotemporal Mechanism-Based Spacetimeformer Network for InSAR Deformation Prediction and Identification of Retrogressive Thaw Slumps in the Chumar River Basin"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0411-0971","authenticated-orcid":false,"given":"Jing","family":"Wang","sequence":"first","affiliation":[{"name":"Key Laboratory of Seismic and Volcanic Hazards, China Earthquake Administration, Beijing 100029, China"},{"name":"Institute of Geology, China Earthquake Administration, Beijing 100029, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiwei","family":"Fan","sequence":"additional","affiliation":[{"name":"Key Laboratory of Seismic and Volcanic Hazards, China Earthquake Administration, Beijing 100029, China"},{"name":"Institute of Geology, China Earthquake Administration, Beijing 100029, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhijie","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Strategic Planning, Chinese Academy of Environmental Planning, Beijing 100012, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuefei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Land Satellite Remote Sensing Application Center, Ministry of Natural Resources, Beijing 100034, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenyu","family":"Nie","sequence":"additional","affiliation":[{"name":"Key Laboratory of Seismic and Volcanic Hazards, China Earthquake Administration, Beijing 100029, China"},{"name":"Institute of Geology, China Earthquake Administration, Beijing 100029, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanmeng","family":"Qi","sequence":"additional","affiliation":[{"name":"Key Laboratory of Seismic and Volcanic Hazards, China Earthquake Administration, Beijing 100029, China"},{"name":"Institute of Geology, China Earthquake Administration, Beijing 100029, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Seismic and Volcanic Hazards, China Earthquake Administration, Beijing 100029, China"},{"name":"Institute of Geology, China Earthquake Administration, Beijing 100029, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2527","DOI":"10.5194\/tc-11-2527-2017","article-title":"A New Map of Permafrost Distribution on the Tibetan Plateau","volume":"11","author":"Zou","year":"2017","journal-title":"Cryosphere"},{"key":"ref_2","first-page":"1","article-title":"Climate Warming Has Led to the Degradation of Permafrost Stability in the Past Half Century over the Qinghai-Tibet Plateau; Frozen Ground","volume":"12","author":"Ran","year":"2017","journal-title":"Cryosphere Discuss."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.earscirev.2010.07.002","article-title":"Permafrost Degradation and Its Environmental Effects on the Tibetan Plateau: A Review of Recent Research","volume":"103","author":"Yang","year":"2010","journal-title":"Earth-Sci. 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