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Advances in Remote Sensing and AI for Monitoring and Mitigating...
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Advances in Remote Sensing and AI for Monitoring and Mitigating Land Subsidence and Secondary Geohazards

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: 28 February 2026 | Viewed by 2111

Special Issue Editors

Dr. Jing Wang

E-Mail Website
Guest Editor
Institute of Geology, China Earthquake Administration, Beijing 100029, China
Interests: remote sensing; deep learning; InSAR; SAR
Dr. Sen Du

E-Mail Website
Guest Editor
Instituto de Geociencias (IGEO), Spanish National Research Council, 28040 Madrid, Spain
Interests: InSAR processing and its application; offset tracking; PloSAR data processing
Special Issues, Collections and Topics in MDPI journals
Dr. Joaquín Menéndez

E-Mail Website
Guest Editor
Instituto de Geociencias (IGEO), CSIC-UCM, 7, 28040 Madrid, Spain
Interests: InSAR processing and its application; data fusion of GNSS and InSAR
Special Issues, Collections and Topics in MDPI journals
Dr. Xiwei Fan

E-Mail Website
Guest Editor
Institute of Geology, China Earthquake Administration, Beijing 100029, China
Interests: seismic building damage detection using optical remote sensing image; seismic risk assessment; thermal infrared remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Land subsidence is a critical geohazard that threatens infrastructure, ecosystems, and public safety globally. It often manifests in diverse environments, including urban areas, mining regions, volcanic zones, permafrost regions, and areas affected by landslides or secondary earthquake hazards. Monitoring, analyzing, and mitigating subsidence is essential for sustainable development and resilience against disasters. Remote sensing technologies, particularly Interferometric Synthetic Aperture Radar (InSAR) and Global Navigation Satellite Systems (GNSSs), have revolutionized subsidence studies by enabling precise, large-scale, and long-term monitoring. Furthermore, advancements in artificial intelligence (AI) and machine learning have enhanced data integration, anomaly detection, and three-dimensional deformation analysis.

This Special Issue aims to highlight recent breakthroughs in remote sensing for land subsidence research. It seeks contributions that explore innovative methods, interdisciplinary studies, and practical applications addressing subsidence-related challenges. This Special Issue aligns with the journal's scope by emphasizing the development of advanced remote sensing applications, geospatial analysis, and AI-driven solutions.

This Special Issue welcomes the submission of original research articles, reviews, and short communications whose scope includes, but is not limited to, the following topics:

  • InSAR and GNSS applications in monitoring land subsidence and secondary earthquake hazards;
  • Advanced algorithms for processing InSAR and GNSS data in subsidence research on infrastructure such as railway lines;
  • Three-dimensional deformation analysis combining InSAR and GNSS data;
  • Deep learning and machine learning methods for automated detection and prediction of subsidence;
  • Monitoring and modeling subsidence in mining regions, urban environments, volcanic zones, earthquake-prone areas, and permafrost areas;
  • Case studies on the impact of land subsidence on infrastructure and ecosystems;
  • Data fusion approaches for integrating InSAR, GNSS, and other geospatial datasets;
  • Multiscale and multitemporal analysis of land subsidence;
  • The integration of remote sensing with geotechnical and hydrological models;
  • The use of multi-source remote sensing data to monitor urban land subsidence and its impacts on socioeconomic factors, the environment, energy consumption, and ecological restoration efforts;
  • Advances in data fusion and visualization techniques for ground subsidence studies. 

This Special Issue provides a platform for advancing our understanding and management of land subsidence, fostering the development of innovative remote sensing technologies that address global geohazard challenges.

Dr. Jing Wang
Dr. Sen Du
Dr. Joaquín Menéndez
Dr. Xiwei Fan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • land subsidence
  • InSAR
  • GNSS deformation monitoring
  • 3D deformation analysis
  • machine learning/deep learning
  • subsidence modeling
  • GPS optical image urban monitoring

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

20 pages, 11124 KB  
Article
RMCMamba: A Multi-Factor High-Speed Railway Bridge Pier Settlement Prediction Method Based on RevIN and MARSHead
by Junjie Liu, Xunqiang Gong, Qi Liang, Zhiping Chen, Tieding Lu, Rui Zhang and Wenfei Mao
Remote Sens. 2025, 17(21), 3596; https://doi.org/10.3390/rs17213596 - 30 Oct 2025
Viewed by 417
Abstract
The precise prediction of high-speed railway bridge pier settlement plays a crucial role in construction, maintenance, and long-term operation; however, current mainstream prediction methods mostly rely on independent analyses based on traditional or hybrid models, neglecting the impact of geological and environmental factors [...] Read more.
The precise prediction of high-speed railway bridge pier settlement plays a crucial role in construction, maintenance, and long-term operation; however, current mainstream prediction methods mostly rely on independent analyses based on traditional or hybrid models, neglecting the impact of geological and environmental factors on subsidence. To address this issue, this paper proposes a multi-factor settlement prediction model for high-speed railway bridge piers named the Reversible Instance Normalization Multi-Scale Adaptive Resolution Stream CMamba, abbreviated as RMCMamba. During the data preprocessing process, the Enhanced PS-InSAR technology is adopted to obtain the time series data of land settlement in the study region. Utilizing the cubic improved Hermite interpolation method to fill the missing values of monitoring and considering the environmental parameters such as groundwater level, temperature, precipitation, etc., a multi-factor high-speed railway bridge pier settlement dataset is constructed. RMCMamba fuses the reversible instance normalization (RevIN) and the multiresolution forecasting head (MARSHead), enhancing the model’s long-range dependence capture capability and solving the time series data distribution drift problem. Experimental results demonstrate that in the multi-factor prediction scenario, RMCMamba achieves an MAE of 0.049 mm and an RMSE of 0.077 mm; in the single-factor prediction scenario, the proposed method reduces errors compared to traditional prediction approaches and other deep learning-based methods, with MAE values improving by 4.8% and 4.4% over the suboptimal method in multi-factor and single-factor scenarios, respectively. Ablation experiments further verify the collaborative advantages of combining reversible instance normalization and the multi-resolution forecasting head, as RMCMamba’s MAE values improve by 5.8% and 4.4% compared to the original model in multi-factor and single-factor scenarios. Hence, the proposed method effectively enhances the prediction accuracy of high-speed railway bridge pier settlement, and the constructed multi-source data fusion framework, along with the model improvement strategy, provides technological and experiential references for relevant fields. Full article
(This article belongs to the Special Issue Advances in Remote Sensing and AI for Monitoring and Mitigating Land Subsidence and Secondary Geohazards)
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26 pages, 20113 KB  
Article
Enhanced Detection of Permafrost Deformation with Machine Learning and Interferometric SAR Along the Qinghai–Tibet Engineering Corridor
by Peng Fan, Hong Lin, Zhengjia Zhang and Heming Deng
Remote Sens. 2025, 17(13), 2231; https://doi.org/10.3390/rs17132231 - 29 Jun 2025
Cited by 1 | Viewed by 1043
Abstract
Interferometric synthetic aperture radar (InSAR) plays a significant role in monitoring permafrost deformation. However, owing to environmental constraints in permafrost regions, some regions exhibit temporal incoherence, which results in deformation with fewer measurement points and difficulties with deformation automatic detection. In this study, [...] Read more.
Interferometric synthetic aperture radar (InSAR) plays a significant role in monitoring permafrost deformation. However, owing to environmental constraints in permafrost regions, some regions exhibit temporal incoherence, which results in deformation with fewer measurement points and difficulties with deformation automatic detection. In this study, a full-coverage deformation rate map of the 10 km buffer of the Qinghai–Tibet Engineering Corridor (QTEC) was generated by combining nine driving factors and the deformation rate of the 5 km buffer along the QTEC based on three machine learning methods. The importance of the factors contributing to ground deformation was explored. The experimental results show that support vector regression (SVR) yielded the best performance (R2 = 0.98, RMSE = 0.76 mm/year, MAE = 0.74 mm/year). The 10 km buffer of deformation data obtained not only preserved the original deformation data well, but it also filled the blank areas in the deformation map. Subsequently, we trained the Faster R-CNN model on the deformation rate map simulated by SVR and used it for the automatic detection of permafrost thaw settlement areas. The results showed that the Faster R-CNN could identify the permafrost thawing slump quickly and accurately. More than 300 deformation areas along the QTEC were detected through our proposed method, with some of these areas located near thaw slump and thermokarst lake regions. This study confirms the significant potential of combining InSAR and deep learning techniques for permafrost degradation monitoring applications. Full article
(This article belongs to the Special Issue Advances in Remote Sensing and AI for Monitoring and Mitigating Land Subsidence and Secondary Geohazards)
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