{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T17:05:57Z","timestamp":1783530357275,"version":"3.55.0"},"reference-count":103,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2023,11,6]],"date-time":"2023-11-06T00:00:00Z","timestamp":1699228800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Important Talent Project of Gansu Province","award":["2022RCXM033"],"award-info":[{"award-number":["2022RCXM033"]}]},{"name":"Important Talent Project of Gansu Province","award":["lzujbky-2021-ey05"],"award-info":[{"award-number":["lzujbky-2021-ey05"]}]},{"name":"Important Talent Project of Gansu Province","award":["42007232"],"award-info":[{"award-number":["42007232"]}]},{"name":"Important Talent Project of Gansu Province","award":["22ZD6FA051"],"award-info":[{"award-number":["22ZD6FA051"]}]},{"name":"Important Talent Project of Gansu Province","award":["YJKJ-2022-04"],"award-info":[{"award-number":["YJKJ-2022-04"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2022RCXM033"],"award-info":[{"award-number":["2022RCXM033"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["lzujbky-2021-ey05"],"award-info":[{"award-number":["lzujbky-2021-ey05"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["42007232"],"award-info":[{"award-number":["42007232"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["22ZD6FA051"],"award-info":[{"award-number":["22ZD6FA051"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["YJKJ-2022-04"],"award-info":[{"award-number":["YJKJ-2022-04"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2022RCXM033"],"award-info":[{"award-number":["2022RCXM033"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["lzujbky-2021-ey05"],"award-info":[{"award-number":["lzujbky-2021-ey05"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42007232"],"award-info":[{"award-number":["42007232"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["22ZD6FA051"],"award-info":[{"award-number":["22ZD6FA051"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["YJKJ-2022-04"],"award-info":[{"award-number":["YJKJ-2022-04"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science and Technology Project of Gansu Province","award":["2022RCXM033"],"award-info":[{"award-number":["2022RCXM033"]}]},{"name":"Science and Technology Project of Gansu Province","award":["lzujbky-2021-ey05"],"award-info":[{"award-number":["lzujbky-2021-ey05"]}]},{"name":"Science and Technology Project of Gansu Province","award":["42007232"],"award-info":[{"award-number":["42007232"]}]},{"name":"Science and Technology Project of Gansu Province","award":["22ZD6FA051"],"award-info":[{"award-number":["22ZD6FA051"]}]},{"name":"Science and Technology Project of Gansu Province","award":["YJKJ-2022-04"],"award-info":[{"award-number":["YJKJ-2022-04"]}]},{"name":"Research on 3D Geological Modeling and Application Technology for Urban Geological Survey","award":["2022RCXM033"],"award-info":[{"award-number":["2022RCXM033"]}]},{"name":"Research on 3D Geological Modeling and Application Technology for Urban Geological Survey","award":["lzujbky-2021-ey05"],"award-info":[{"award-number":["lzujbky-2021-ey05"]}]},{"name":"Research on 3D Geological Modeling and Application Technology for Urban Geological Survey","award":["42007232"],"award-info":[{"award-number":["42007232"]}]},{"name":"Research on 3D Geological Modeling and Application Technology for Urban Geological Survey","award":["22ZD6FA051"],"award-info":[{"award-number":["22ZD6FA051"]}]},{"name":"Research on 3D Geological Modeling and Application Technology for Urban Geological Survey","award":["YJKJ-2022-04"],"award-info":[{"award-number":["YJKJ-2022-04"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Interferometric synthetic aperture radar (InSAR) technology has become one of the mainstream techniques for active landslide identification over a large area. However, the method for interpreting anomalous deformation areas derived from InSAR data is still mainly manual delineation through human\u2013computer interaction. This study focuses on using a deep learning semantic segmentation model to identify the boundaries of anomalous deformation areas automatically. We experimented with the delineation results based on an InSAR deformation map, hot spot map, and different combinations of topographic datasets to build the optimal model. The result indicates that the hot spot map, aspect, and Google Earth image as input features based on the U-Net model can achieve the best performance, with the precision, recall, F1 score, and intersection over union (IoU) being 0.822, 0.835, 0.823, and 0.705, respectively. Our method promotes the development of identifying active landslides using InSAR technology automatically and rapidly at a regional scale. Moreover, applying a new method for automatically and rapidly identifying potential landslides in susceptible areas is necessary for landslide hazard mitigation and risk management.<\/jats:p>","DOI":"10.3390\/rs15215262","type":"journal-article","created":{"date-parts":[[2023,11,6]],"date-time":"2023-11-06T13:24:53Z","timestamp":1699277093000},"page":"5262","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Automatic Identification for the Boundaries of InSAR Anomalous Deformation Areas Based on Semantic Segmentation Model"],"prefix":"10.3390","volume":"15","author":[{"given":"Yiwen","family":"Liang","sequence":"first","affiliation":[{"name":"Aerial Photogrammetry and Remote Sensing Bureau of China Administration of Coal Geology, Xi\u2019an 710199, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Technology & Innovation Centre for Environmental Geology and Geohazards Prevention, School of Earth Sciences, Lanzhou University, Lanzhou 730000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanxi","family":"Li","sequence":"additional","affiliation":[{"name":"Technology & Innovation Centre for Environmental Geology and Geohazards Prevention, School of Earth Sciences, Lanzhou University, Lanzhou 730000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaqi","family":"Xiong","sequence":"additional","affiliation":[{"name":"Aerial Photogrammetry and Remote Sensing Bureau of China Administration of Coal Geology, Xi\u2019an 710199, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"113620","DOI":"10.1016\/j.rse.2023.113620","article-title":"Inferring slip-surface geometry and volume of creeping landslides based on InSAR: A case study in Jinsha River basin","volume":"294","author":"Kang","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"110324","DOI":"10.1016\/j.asoc.2023.110324","article-title":"An Explainable AI (XAI) Model for Landslide Susceptibility Modeling","volume":"142","author":"Pradhan","year":"2023","journal-title":"Appl. Soft Comput."},{"key":"ref_3","first-page":"209","article-title":"Remote sensing techniques in disaster management: Amynteon mine landslides, Greece","volume":"Volume 11","author":"Karagianni","year":"2019","journal-title":"Intelligent Systems for Crisis Management: Gi4DM 2018"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.enggeo.2011.06.001","article-title":"Toward the next generation of research on earthquake-induced landslides: Current issues and future challenges","volume":"122","author":"Wasowski","year":"2011","journal-title":"Eng. Geol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2155","DOI":"10.1007\/s10346-023-02080-0","article-title":"Landslides triggered by the 10 June 2022 Maerkang earthquake swarm, Sichuan, China: Spatial distribution and tectonic significance","volume":"20","author":"Chen","year":"2023","journal-title":"Landslides"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"108635","DOI":"10.1016\/j.geomorph.2023.108635","article-title":"Earthquake-induced landslide hazard assessment in the Vrancea Seismic Region (Eastern Carpathians, Romania): Constraints and perspectives","volume":"427","author":"Micu","year":"2023","journal-title":"Geomorphology"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.enggeo.2008.02.008","article-title":"Integrated geophysical and geomorphological approach to investigate the snowmelt-triggered landslide of Bosco Piccolo village (Basilicata, southern Italy)","volume":"98","author":"Naudet","year":"2008","journal-title":"Eng. Geol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1007\/s10346-022-01979-4","article-title":"Two-dimensional deformation monitoring for spatiotemporal evolution and failure mode of Lashagou landslide group, Northwest China","volume":"20","author":"Zhang","year":"2023","journal-title":"Landslides"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1561","DOI":"10.1007\/s11069-023-06067-x","article-title":"Megalandslides and deglaciation: Modelling of two case studies in the Central Andes","volume":"118","author":"Tobar","year":"2023","journal-title":"Nat. Hazards"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2199","DOI":"10.1007\/s10346-023-02109-4","article-title":"Landslides triggered by an extraordinary rainfall event in Central Italy on September 15, 2022","volume":"20","author":"Donnini","year":"2023","journal-title":"Landslides"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1038\/s41597-023-02336-3","article-title":"Inventory of landslides triggered by an extreme rainfall event in Marche-Umbria, Italy, on 15 September 2022","volume":"10","author":"Santangelo","year":"2023","journal-title":"Sci. Data"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1007\/s10346-022-02015-1","article-title":"Analysis of the cascading rainfall\u2013landslide\u2013tsunami event of June 29th, 2022, Todos los Santos Lake, Chile","volume":"20","author":"Espinoza","year":"2023","journal-title":"Landslides"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1007\/s10346-019-01265-w","article-title":"Heifangtai loess landslide type and failure mode analysis with ascending and descending Spot-mode TerraSAR-X datasets","volume":"17","author":"Liu","year":"2020","journal-title":"Landslides"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"107030","DOI":"10.1016\/j.enggeo.2023.107030","article-title":"Failure mechanism and movement process of three loess landslides due to freeze-thaw cycle in the Fangtai village, Yongjing County, Chinese Loess Plateau","volume":"315","author":"Kong","year":"2023","journal-title":"Eng. Geol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1007\/s10064-022-03062-7","article-title":"Potential failure patterns of a large landslide complex in the Three Gorges Reservoir area","volume":"82","author":"Dong","year":"2023","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1731","DOI":"10.1007\/s10346-023-02056-0","article-title":"A novel method to evaluate the time-dependent stability of reservoir landslides: Exemplified by Outang landslide in the Three Gorges Reservoir","volume":"20","author":"Zou","year":"2023","journal-title":"Landslides"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Kalantar, B., Ueda, N., Saeidi, V., Ahmadi, K., Halin, A.A., and Shabani, F. (2020). Landslide Susceptibility Mapping: Machine and Ensemble Learning Based on Remote Sensing Big Data. Remote Sens., 12.","DOI":"10.3390\/rs12111737"},{"key":"ref_18","unstructured":"Liao, M., Zhang, L., and Shi, X. (2017). Methods and Practices of Landslide Deformation Monitoring with SAR, Science Press. (In Chinese)."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.tecto.2014.10.005","article-title":"Pre-eruptive ground deformation of Azerbaijan mud volcanoes detected through satellite radar interferometry (DInSAR)","volume":"637","author":"Antonielli","year":"2014","journal-title":"Tectonophysics"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.enggeo.2014.03.003","article-title":"Investigating landslides and unstable slopes with satellite Multi Temporal Interferometry: Current issues and future perspectives","volume":"174","author":"Wasowski","year":"2014","journal-title":"Eng. Geol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1146\/annurev.earth.28.1.169","article-title":"Synthetic aperture radar interferometry to measure Earth\u2019s surface topography and its deformation","volume":"28","author":"Rosen","year":"2000","journal-title":"Annu. Rev. Earth Planet. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1273","DOI":"10.1007\/s10346-015-0660-8","article-title":"Detection of geohazards in the Bailong River Basin using synthetic aperture radar interferometry","volume":"13","author":"Zhang","year":"2016","journal-title":"Landslides"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Su, X., Zhang, Y., Meng, X., Rehman, M.U., Khalid, Z., and Yue, D. (2022). Updating Inventory, Deformation, and Development Characteristics of Landslides in Hunza Valley, NW Karakoram, Pakistan by SBAS-InSAR. Remote Sens., 14.","DOI":"10.3390\/rs14194907"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"106033","DOI":"10.1016\/j.enggeo.2021.106033","article-title":"Integration of Sentinel-1 and ALOS\/PALSAR-2 SAR datasets for mapping active landslides along the Jinsha River corridor, China","volume":"284","author":"Liu","year":"2021","journal-title":"Eng. Geol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"39093","DOI":"10.1007\/s11356-022-25055-5","article-title":"Identification and deformation analysis of potential landslides after the Jiuzhaigou earthquake by SBAS-InSAR","volume":"30","author":"Chang","year":"2023","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2110","DOI":"10.1109\/JSTARS.2022.3228948","article-title":"Applicability Analysis of Potential Landslide Identification by InSAR in Alpine-Canyon Terrain\u2014Case Study on Yalong River","volume":"15","author":"Dai","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2185120","DOI":"10.1080\/19475705.2023.2185120","article-title":"An identification method of potential landslide zones using InSAR data and landslide susceptibility","volume":"14","author":"He","year":"2023","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1016\/j.geomorph.2004.11.001","article-title":"The Effectiveness of Hillshade Maps and Expert Knowledge in Mapping Old Deep-Seated Landslides","volume":"67","author":"Poesen","year":"2005","journal-title":"Geomorphology"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1080\/01431161.2010.536185","article-title":"Persistent Scatterers Interferometry Hotspot and Cluster Analysis (PSI-HCA) for detection of extremely slow-moving landslides","volume":"33","author":"Lu","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhang, J., Zhu, W., Cheng, Y., and Li, Z. (2021). Landslide Detection in the Linzhi\u2013Ya\u2019an Section along the Sichuan\u2013Tibet Railway Based on InSAR and Hot Spot Analysis Methods. Remote Sens., 13.","DOI":"10.3390\/rs13183566"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Xun, Z., Zhao, C., Kang, Y., Liu, X., Liu, Y., and Du, C. (2022). Automatic Extraction of Potential Landslides by Integrating an Optical Remote Sensing Image with an InSAR-Derived Deformation Map. Remote Sens., 14.","DOI":"10.3390\/rs14112669"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2383","DOI":"10.1007\/s11069-022-05642-y","article-title":"Comparison of pixel, sub-pixel and object-based image analysis techniques for co-seismic landslides detection in seismically active area in Lesser Himalaya, Pakistan","volume":"115","author":"Saba","year":"2023","journal-title":"Nat. Hazards"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ghorbanzadeh, O., Gholamnia, K., and Ghamisi, P. (2022). The application of ResU-net and OBIA for landslide detection from multi-temporal sentinel-2 images. Big Earth Data, 1\u201326.","DOI":"10.1080\/20964471.2022.2031544"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.isprsjprs.2009.06.004","article-title":"Object Based Image Analysis for Remote Sensing","volume":"65","author":"Blaschke","year":"2010","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"107822","DOI":"10.1016\/j.compag.2023.107822","article-title":"A comparison between Pixel-based deep learning and Object-based image analysis (OBIA) for individual detection of cabbage plants based on UAV Visible-light images","volume":"209","author":"Ye","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Chen, X., Yao, X., Zhou, Z., Liu, Y., Yao, C., and Ren, K. (2022). DRs-UNet: A Deep Semantic Segmentation Network for the Recognition of Active Landslides from InSAR Imagery in the Three Rivers Region of the Qinghai\u2013Tibet Plateau. Remote Sens., 14.","DOI":"10.3390\/rs14081848"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/j.neucom.2019.02.003","article-title":"Survey on semantic segmentation using deep learning techniques","volume":"338","author":"Lateef","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"113545","DOI":"10.1016\/j.rse.2023.113545","article-title":"Automatic detection and classification of land subsidence in deltaic metropolitan areas using distributed scatterer InSAR and Oriented R-CNN","volume":"290","author":"Wu","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Wang, Z., Sun, T., Hu, K., Zhang, Y., Yu, X., and Li, Y. (2022). A Deep Learning Semantic Segmentation Method for Landslide Scene Based on Transformer Architecture. Sustainability, 14.","DOI":"10.3390\/su142316311"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1007\/s10346-022-01983-8","article-title":"Sematic segmentation of loess landslides with STAPLE mask and fully connected conditional random field","volume":"20","author":"Li","year":"2023","journal-title":"Landslides"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"104388","DOI":"10.1016\/j.cageo.2019.104388","article-title":"Landslide detection based on contour-based deep learning framework in case of national scale of Nepal in 2015","volume":"135","author":"Yu","year":"2020","journal-title":"Comput. Geosci."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Liu, Y., Yao, X., Gu, Z., Zhou, Z., Liu, X., Chen, X., and Wei, S. (2022). Study of the Automatic Recognition of Landslides by Using InSAR Images and the Improved Mask R-CNN Model in the Eastern Tibet Plateau. Remote Sens., 14.","DOI":"10.3390\/rs14143362"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Liu, P., Wei, Y., Wang, Q., Chen, Y., and Xie, J. (2020). Research on Post-Earthquake Landslide Extraction Algorithm Based on Improved U-Net Model. Remote Sens., 12.","DOI":"10.3390\/rs12050894"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Zhang, T., Zhang, W., Cao, D., Yi, Y., and Wu, X. (2022). A New Deep Learning Neural Network Model for the Identification of InSAR Anomalous Deformation Areas. Remote Sens., 14.","DOI":"10.3390\/rs14112690"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Guo, H., Yi, B., Yao, Q., Gao, P., Li, H., Sun, J., and Zhong, C. (2022). Identification of Landslides in Mountainous Area with the Combination of SBAS-InSAR and Yolo Model. Sensors, 22.","DOI":"10.3390\/s22166235"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Niu, C., Yin, W., Xue, W., Sui, Y., Xun, X., Zhou, X., Zhang, S., and Xue, Y. (2023). Multi-Window Identification of Landslide Hazards Based on InSAR Technology and Factors Predisposing to Disasters. Land, 12.","DOI":"10.3390\/land12010173"},{"key":"ref_48","first-page":"457","article-title":"Geological hazards effects of uplift of Qinghai-Tibet Plateau","volume":"19","author":"Peng","year":"2004","journal-title":"Adv. Earth Sci."},{"key":"ref_49","unstructured":"Yin, Z., Qin, X., Zhao, X., Li, X., Cheng, G., Wei, G., Shi, L., and Yuan, C. (2016). Temporal and Spatial Evolution and Triggering Mechanism of Landslide and Debris Flow in the Upper Reaches of the Yellow River, Science Press. (In Chinese)."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.scitotenv.2019.04.140","article-title":"Mapping and characterizing displacements of active loess slopes along the upstream Yellow River with multi-temporal InSAR datasets","volume":"674","author":"Shi","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1016\/0277-3791(91)90041-R","article-title":"The environmental effects of the uplift of the Qinghai-Xizang Plateau","volume":"10","author":"Li","year":"1991","journal-title":"Quat. Sci. Rev."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1038\/ngeo777","article-title":"Rapid fluvial incision along the Yellow River during headward basin integration","volume":"3","author":"Craddock","year":"2010","journal-title":"Nat. Geosci."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Guo, X., Wei, J., Lu, Y., Song, Z., and Liu, H. (2020). Geomorphic Effects of a Dammed Pleistocene Lake Formed by Landslides along the Upper Yellow River. Water, 12.","DOI":"10.3390\/w12051350"},{"key":"ref_54","first-page":"779","article-title":"Characteristics of landslides in upper reaches of Yellow River with multiple data of remote sensing","volume":"21","author":"Yin","year":"2013","journal-title":"J. Eng. Geol."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/j.quaint.2015.06.021","article-title":"Optical dating of landslide-dammed lake deposits in the upper Yellow River, Qinghai-Tibetan Plateau, China","volume":"392","author":"Guo","year":"2016","journal-title":"Quat. Int."},{"key":"ref_56","first-page":"147","article-title":"Xijitan landslide in guide basin in the upper reaches of the Yellow River and its Dammed Lakes","volume":"4","author":"Qin","year":"2015","journal-title":"Geophys. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1961","DOI":"10.1007\/s10346-017-0846-3","article-title":"Investigating a reservoir bank slope displacement history with multi-frequency satellite SAR data","volume":"14","author":"Shi","year":"2017","journal-title":"Landslides"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1029\/2008GL034654","article-title":"A multi-temporal InSAR method incorporating both persistent scatterer and small baseline approaches","volume":"35","author":"Hooper","year":"2008","journal-title":"Geophys. Res. Lett."},{"key":"ref_59","first-page":"4362","article-title":"Interferometric point target analysis for deformation mapping","volume":"7","author":"Werner","year":"2003","journal-title":"IEEE Geosci. Remote Sens. Soci."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Liu, H., Zhou, B., Bai, Z., Zhao, W., Zhu, M., Zheng, K., Yang, S., and Li, G. (2023). Applicability Assessment of Multi-Source DEM-Assisted InSAR Deformation Monitoring Considering Two Topographical Features. Land, 12.","DOI":"10.3390\/land12071284"},{"key":"ref_61","unstructured":"(2023, October 17). Alaska Satellite Facility\u2014Distributed Active Archive Center. Available online: https:\/\/asf.alaska.edu\/data-sets\/derived-data-sets\/alos-palsar-rtc\/alos-palsar-radiometric-terrain-correction\/."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"7898","DOI":"10.1080\/01431161.2019.1607612","article-title":"A Sentinel-1 based hot-spot analysis: Landslide mapping in north-western Italy","volume":"40","author":"Solari","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Zhu, K., Xu, P., Cao, C., Zheng, L., Liu, Y., and Dong, X. (2021). Preliminary identification of geological hazards from Songpinggou to Feihong in Mao County along the Minjiang River using SBAS-InSAR technique integrated multiple spatial analysis methods. Sustainability, 13.","DOI":"10.3390\/su13031017"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.isprsjprs.2019.08.004","article-title":"Landslides detection through optimized hot spot analysis on persistent scatterers and distributed scatterers","volume":"156","author":"Lu","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1111\/j.1538-4632.1995.tb00912.x","article-title":"Local spatial autocorrelation statistics: Distributional issues and an application","volume":"27","author":"Ord","year":"2015","journal-title":"Geogr. Anal."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1111\/j.1538-4632.1992.tb00261.x","article-title":"The analysis of spatial association by use of distance statistics","volume":"24","author":"Getis","year":"1992","journal-title":"Geogr. Anal."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1007\/s10346-013-0432-2","article-title":"Quantitative hazard and risk assessment for slow-moving landslides from Persistent Scatterer Interferometry","volume":"11","author":"Lu","year":"2014","journal-title":"Landslides"},{"key":"ref_68","unstructured":"Silverman, B.W. (1986). Density Estimation for Statistics and Data Analysis, Routledge."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"2182057","DOI":"10.1080\/15481603.2023.2182057","article-title":"Mapping landslides through a temporal lens: An insight toward multi-temporal landslide mapping using the u-net deep learning model","volume":"60","author":"Bhuyan","year":"2023","journal-title":"GISci. Remote Sens."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"552","DOI":"10.1080\/17538947.2023.2177359","article-title":"A landslide extraction method of channel attention mechanism U-Net network based on Sentinel-2A remote sensing images","volume":"16","author":"Chen","year":"2023","journal-title":"Int. J. Digit. Earth"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"105189","DOI":"10.1016\/j.catena.2021.105189","article-title":"Convolutional neural networks applied to semantic segmentation of landslide scars","volume":"201","author":"Bragagnolo","year":"2021","journal-title":"Catena"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1209","DOI":"10.1007\/s10346-022-01861-3","article-title":"Landslide detection in the Himalayas using machine learning algorithms and U-Net","volume":"19","author":"Meena","year":"2022","journal-title":"Landslides"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"Segnet: A deep convolutional encoder-decoder architecture for image segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"101373","DOI":"10.1016\/j.ecoinf.2021.101373","article-title":"Convolutional neural network based encoder-decoder architectures for semantic segmentation of plants","volume":"64","author":"Kolhar","year":"2021","journal-title":"Ecol. Inform."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"215","DOI":"10.24843\/EJES.2019.v13.i02.p09","article-title":"An application of SegNet for detecting landslide areas by using fully polarimetric SAR data","volume":"13","author":"Antara","year":"2019","journal-title":"Ecotrophic"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"1293","DOI":"10.1007\/s12145-020-00516-y","article-title":"Person identification with aerial imaginary using SegNet based semantic segmentation","volume":"13","author":"Manickam","year":"2020","journal-title":"Earth Sci. Inform."},{"key":"ref_78","unstructured":"Chollet, F. (2021). Deep Learning with Python, Manning."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1007\/s11069-021-04838-y","article-title":"Landslide detection using visualization techniques for deep convolutional neural network models","volume":"109","author":"Demir","year":"2021","journal-title":"Nat. Hazards"},{"key":"ref_80","first-page":"253","article-title":"Satellite radar interferometry for deformation monitoring: A priori assessment of feasibility and accuracy","volume":"6","author":"Hanssen","year":"2005","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_81","unstructured":"Colesanti, C., and Wasowski, J. (July, January 28). Satellite SAR interferometry for wide-area slope hazard detection and site-specific monitoring of slow landslides. Proceedings of the Ninth International Symposium on Landslides, Rio de Janeiro, Brazil."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"633342","DOI":"10.3389\/feart.2020.633342","article-title":"Using Google Earth images to extract dense landslides induced by historical earthquakes at the Southwest of Ordos, China","volume":"8","author":"Du","year":"2021","journal-title":"Front. Earth Sci."},{"key":"ref_83","unstructured":"Singhroy, V. (2009). Landslides\u2013Disaster Risk Reduction, Springer."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.earscirev.2012.02.001","article-title":"Landslide inventory maps: New tools for an old problem","volume":"112","author":"Guzzetti","year":"2012","journal-title":"Earth-Sci. Rev."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"2644","DOI":"10.1109\/JSTARS.2022.3161383","article-title":"Feature-based constraint deep CNN method for mapping rainfall-induced landslides in remote regions with mountainous terrain: An application to Brazil","volume":"15","author":"Xu","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_86","first-page":"242","article-title":"Mini-batch semi-stochastic gradient descent in the proximal setting","volume":"10","author":"Liu","year":"2015","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"104860","DOI":"10.1016\/j.cageo.2021.104860","article-title":"Landslide susceptibility prediction based on image semantic segmentation","volume":"155","author":"Du","year":"2021","journal-title":"Comput. Geosci."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"107542","DOI":"10.1016\/j.compag.2022.107542","article-title":"U2ESPNet\u2014A lightweight and high-accuracy convolutional neural network for real-time semantic segmentation of visible branches","volume":"204","author":"Wan","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"1337","DOI":"10.1007\/s10346-020-01353-2","article-title":"Landslide detection from an open satellite imagery and digital elevation model dataset using attention boosted convolutional neural networks","volume":"17","author":"Ji","year":"2020","journal-title":"Landslides"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"108734","DOI":"10.1016\/j.compeleceng.2023.108734","article-title":"High-resolution remote sensing images semantic segmentation using improved UNet and SegNet","volume":"108","author":"Wang","year":"2023","journal-title":"Comput. Electr. Eng."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"4139","DOI":"10.1007\/s10064-018-1389-0","article-title":"A new slope unit extraction method for regional landslide analysis based on morphological image analysis","volume":"78","author":"Wang","year":"2019","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"3715","DOI":"10.1007\/s10346-021-01756-9","article-title":"Efficient and automatic extraction of slope units based on multi-scale segmentation method for landslide assessments","volume":"18","author":"Huang","year":"2021","journal-title":"Landslides"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"1641","DOI":"10.1080\/10106049.2019.1582716","article-title":"Landslide susceptibility assessment using different slope units based on the evidential belief function model","volume":"35","author":"Chen","year":"2020","journal-title":"Geocarto Int."},{"key":"ref_94","unstructured":"Brunsden, D., and Prior, E. (1984). Slope Instability, Wiley."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"107062","DOI":"10.1016\/j.enggeo.2023.107062","article-title":"Deformation process and kinematic evolution of the large Daxiaowan earthflow in the NE Qinghai-Tibet Plateau","volume":"316","author":"Li","year":"2023","journal-title":"Eng. Geol."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"106000","DOI":"10.1016\/j.enggeo.2021.106000","article-title":"Landslide mapping using object-based image analysis and open source tools","volume":"282","author":"Amatya","year":"2021","journal-title":"Eng. Geol."},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"\u0160iljeg, A., Pan\u0111a, L., Domazetovi\u0107, F., Mari\u0107, I., Ga\u0161parovi\u0107, M., Borisov, M., and Milo\u0161evi\u0107, R. (2022). Comparative Assessment of Pixel and Object-Based Approaches for Mapping of Olive Tree Crowns Based on UAV Multispectral Imagery. Remote Sens., 14.","DOI":"10.3390\/rs14030757"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"103414","DOI":"10.1016\/j.autcon.2020.103414","article-title":"Improved Mask R-CNN with distance guided intersection over union for GPR signature detection and segmentation","volume":"121","author":"Hou","year":"2021","journal-title":"Autom. Constr."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"929","DOI":"10.1007\/s10346-021-01843-x","article-title":"Landslide detection using deep learning and object-based image analysis","volume":"19","author":"Ghorbanzadeh","year":"2022","journal-title":"Landslides"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"111370","DOI":"10.1016\/j.rse.2019.111370","article-title":"Improved correction of seasonal tropospheric delay in InSAR observations for landslide deformation monitoring","volume":"233","author":"Dong","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_101","unstructured":"Wasowski, J., and Bovenga, F. (2022). Landslide Hazards, Risks, and Disasters, Elsevier."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"2459","DOI":"10.1007\/s10346-022-01915-6","article-title":"Landslide detection from bitemporal satellite imagery using attention-based deep neural networks","volume":"19","author":"Amankwah","year":"2022","journal-title":"Landslides"},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"Yang, S., Wang, Y., Wang, P., Mu, J., Jiao, S., Zhao, X., Wang, Z., Wang, K., and Zhu, Y. (2022). Automatic Identification of Landslides Based on Deep Learning. Appl. Sci., 12.","DOI":"10.3390\/app12168153"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/21\/5262\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:18:28Z","timestamp":1760131108000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/21\/5262"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,6]]},"references-count":103,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2023,11]]}},"alternative-id":["rs15215262"],"URL":"https:\/\/doi.org\/10.3390\/rs15215262","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,6]]}}}