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Remote Sensing
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Artificial Intelligence and Remote Sensing for Geohazards
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Artificial Intelligence and Remote Sensing for Geohazards

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: 31 August 2026 | Viewed by 9894

Special Issue Editors

Dr. Pierluigi Confuorto

E-Mail Website
Guest Editor
Department of Earth Sciences, University of Florence, Via La Pira, 4, 50121 Florence, Italy
Interests: landslides; engineering geology; monitoring; civil engineering; remote sensing; natural hazards; InSAR; satellite-based monitoring; GIS; subsidence; modelling of environmental processes
Special Issues, Collections and Topics in MDPI journals
Dr. Silvia Bianchini

E-Mail Website
Guest Editor
Department of Earth Sciences, University of Florence, Via La Pira, 4, 50121 Florence, Italy
Interests: natural hazards; geohazards mapping and monitoring; remote sensing data; InSAR; cultural heritage
Special Issues, Collections and Topics in MDPI journals
Dr. Chiara Martinello

E-Mail Website
Guest Editor
Department of Earth and Marine Sciences, University of Palermo, Via Archirafi 22, 90123 Palermo, Italy
Interests: landslides; GIS analysis; geomorphological mapping; GIS and environmental modeling; GIS and remote sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Davide Festa

E-Mail Website
Guest Editor
Department für Geodäsie und Geoinformation, TU Wien, Wiedner Hauptstraße 8-10, 1040 Vienna, Austria
Interests: landslide modeling; mapping; machine learning; InSAR; time series analysis; satellite-based monitoring; SAR processing; microwave remote sensing

Special Issue Information

Dear Colleagues,

Geohazards, or geological hazards, can be defined as “events caused by geological, geomorphological, and climatic conditions or processes that represent serious threats to human lives, property, and the natural and built environment”. According to the Emergency Events Database (https://public.emdat.be/), in 2023, about 199 geohazards occurred, claiming the life of more than 65000 people and affecting almost 38 million people in total. The detection and mapping of geological hazards are paramount activities for land management and risk reduction policies around the world. Remote sensing technologies can be of benefit due to a high spatial and temporal coverage, allowing relevant information centered around the investigation, characterization, monitoring, and modeling of geohazards to be obtained. Alongside remote sensing, artificial intelligence and machine learning represent a significant innovation for the analysis of geohazards. This kind of approaches has widely demonstrated their suitability in many scientific fields, being characterized by high accuracy and specific advantages in different study areas and for different sets of factors. Machine learning is being increasingly implemented on remotely sensed data, providing support to the processing of datasets; the classification of imagery; the modeling of hazards, susceptibilities, or risks; the analysis of time series; and the rapid implementation of big data. This Remote Sensing Special Issue invites papers that apply machine learning techniques to remotely sensed data to address challenges around geohazards. This includes topics such as:

  • The application of remotely sensed data to physically and statistically based hazard and risk models;
  • The processing of remote sensing data with machine learning algorithms;
  • The machine learning classification of remote sensing data;
  • The processing of RS time series;
  • Machine learning for the mapping and/or monitoring of geohazards;
  • Landslide or subsidence analysis.

Dr. Pierluigi Confuorto
Dr. Silvia Bianchini
Dr. Chiara Martinello
Dr. Davide Festa
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

  • modeling
  • monitoring
  • landslides
  • subsidence
  • geohazard
  • susceptibility
  • risk analysis
  • GIS
  • machine learning

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Related Special Issue

Published Papers (5 papers)

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Research

30 pages, 3212 KB  
Article
Application of PSInSAR Monitoring for Large-Scale Landslide with Persistent Scatterers from Deep Learning Classification
by Yu-Heng Tai, Chi-Chuan Lo, Fuan Tsai and Chung-Pai Chang
Remote Sens. 2026, 18(8), 1181; https://doi.org/10.3390/rs18081181 - 15 Apr 2026
Abstract
The Persistent Scatterers InSAR (PSInSAR) technology, which utilizes pixels with stable phases to extract ground deformation, is an effective tool for large-scale, long-period surface monitoring applications. It has been widely applied to land subsidence monitoring, earthquake research, and infrastructure risk management. Furthermore, some [...] Read more.
The Persistent Scatterers InSAR (PSInSAR) technology, which utilizes pixels with stable phases to extract ground deformation, is an effective tool for large-scale, long-period surface monitoring applications. It has been widely applied to land subsidence monitoring, earthquake research, and infrastructure risk management. Furthermore, some studies have successfully employed this method to monitor the progressive motion of creeping in landslide areas. However, these regions containing active landslides are usually covered by canopy layers, which cause low coherence in InSAR processing and reduce the number of stable pixels, thereby preventing long-term period monitoring in those areas. In this study, the supervised deep learning model, U-Net, based on a convolutional neural network, is applied to the differential InSAR dataset acquired from Sentinel-1 to improve persistent scatterer selection. A well-processed PSInSAR result, utilizing 55 Sentinel-1 images acquired from 5 November 2014 to 19 December 2017, is introduced as a dataset for model training. The pixel-based Persistent Scatterer (PS) labels used for model training are identified using the StaMPS software. The model is designed to identify the distributed scatterer (iDS) index using a single pair of SAR images. As a result, more iDS pixels can be obtained from a single interferogram, indicating a significant improvement over the StaMPS algorithm. The line-of-sight velocity and time series of PS pixels from the model prediction show a long-term uplift on the upper slope, which represents downslope sliding in the target area. Furthermore, some iDS pixels exhibit a seasonal deformation on the lower part of the slope. The capability for these additional deformation analyses underscores the potential of this new deep-learning-based approach. Full article
(This article belongs to the Special Issue Artificial Intelligence and Remote Sensing for Geohazards)
34 pages, 35610 KB  
Article
Integrating InSAR and Channel Steepness for AI-Based Coseismic Landslide Modeling in the Nepal Himalaya
by Rajesh Silwal, Guoquan Wang, Sabal KC, Rabin Rimal and Sagar Rawal
Remote Sens. 2026, 18(8), 1151; https://doi.org/10.3390/rs18081151 - 13 Apr 2026
Abstract
Earthquake-induced landslides in active orogens such as the Nepal Himalaya pose severe threats to lives, infrastructure, and post-disaster recovery. While machine learning (ML) and deep learning (DL) approaches to coseismic landslide susceptibility mapping have advanced considerably, spaceborne interferometric synthetic aperture radar (InSAR) products, [...] Read more.
Earthquake-induced landslides in active orogens such as the Nepal Himalaya pose severe threats to lives, infrastructure, and post-disaster recovery. While machine learning (ML) and deep learning (DL) approaches to coseismic landslide susceptibility mapping have advanced considerably, spaceborne interferometric synthetic aperture radar (InSAR) products, particularly line-of-sight (LOS) displacement and coherence-based damage proxy maps (DPMs), remain underutilized in event-based frameworks. This study develops and evaluates a multi-factor coseismic landslide probability model that integrates InSAR-derived deformation metrics with geomorphic and hydrologic predictors to support rapid post-earthquake hazard assessment. Using the 25 April 2015 Mw 7.8 Gorkha earthquake as a case study, LOS displacement was derived from ALOS-2 PALSAR-2 ScanSAR interferometry, and the normalized channel steepness index (Ksn) was computed from a digital elevation model. Fourteen conditioning factors were used to train five architectures: Random Forest (RF), XGBoost, CNN, U-Net, and DeepLabV3. Spatial autocorrelation was mitigated using a leave-one-basin-out three-fold spatial cross-validation strategy, with models evaluated on a patch-based domain comprising 655,360 pixels at a positive-class prevalence of 6.35%, establishing a no-skill AUC-PR baseline of 0.0635. InSAR integration consistently improved model performance under high class imbalance, increasing AUC-PR across all models by 7.8% to 17.3%. Random Forest achieved the highest AUC-PR (0.7940, nearly 12.5 times the baseline) and CSI (0.3027), providing the best balance between landslide recall (88.09%) and non-landslide specificity (88.68%) with the lowest false alarm rate (11.32%). XGBoost attained the highest AUC-ROC (0.9501) but exhibited lower recall (83.73%) and poorer calibration (Brier = 0.1397). Among DL models, DeepLabV3 produced the best-calibrated probabilities (Brier = 0.0693) and the highest CSI (0.2307), while U-Net offered the most balanced DL performance and CNN achieved the highest recall (92.40%) at the expense of elevated false alarms. Permutation feature importance identified Ksn as the dominant predictor, highlighting the strong tectono-geomorphic control on coseismic landslide occurrence. These results demonstrate that integrating InSAR-derived products substantially enhances landslide hazard assessment and supports more reliable rapid response in the Nepal Himalaya. Full article
(This article belongs to the Special Issue Artificial Intelligence and Remote Sensing for Geohazards)
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28 pages, 84824 KB  
Article
Deep Learning-Based Multitemporal Spatial Analytics for Assessing Reclamation Compliance of Coal Mining Permits in Kalimantan with Satellite Images
by Koni D. Prasetya and Fuan Tsai
Remote Sens. 2025, 17(20), 3477; https://doi.org/10.3390/rs17203477 - 18 Oct 2025
Cited by 1 | Viewed by 2184
Abstract
Monitoring reclamation compliance is important to ensure mining activities follow environmental regulations and reduce land degradation. Yet, few studies directly assess compliance by linking multitemporal satellite data with mining permits. This study presents a multitemporal spatial analytics approach to evaluate reclamation compliance in [...] Read more.
Monitoring reclamation compliance is important to ensure mining activities follow environmental regulations and reduce land degradation. Yet, few studies directly assess compliance by linking multitemporal satellite data with mining permits. This study presents a multitemporal spatial analytics approach to evaluate reclamation compliance in coal mining permit areas in South Kalimantan, Indonesia. Using satellite imagery from 2016 to 2021, a U-Net-based deep learning classification model classified five land surface types (topsoil, subsoil, vegetation, coal bodies and water bodies) with 0.94 accuracy and a Kappa statistic of 0.91. However, this relatively high accuracy was influenced by the dominance of vegetation compared to more challenging classes such as topsoil and subsoil, which remain subject to misclassification. Analysis of temporal transitions revealed patterns of surface disturbance and delayed reclamation, particularly shown by increased subsoil and reduced vegetation. These changes were integrated with coal mining permit boundaries to derived compliance ratios (CR) ranging from 0.32 to 1.44 across nine permit holders, most of which showed moderate to excellent compliance levels. This indicates that reclamation efforts have been generally being implemented, with several permit holders exceeding expectations, while a few others still need to improve. Reclamation Activity Index (RAI) was developed to classify annual performance and showed strong alignment with the U-Net-based deep learning classification model for surface change trends. The proposed approach provides a scalable and practical tool to support evidence-based monitoring and enforcement of mining reclamation policies. Full article
(This article belongs to the Special Issue Artificial Intelligence and Remote Sensing for Geohazards)
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27 pages, 18217 KB  
Article
Landslide Identification in UAV Images Through Recognition of Landslide Boundaries and Ground Surface Cracks
by Zhan Cheng, Wenping Gong, Michel Jaboyedoff, Jun Chen, Marc-Henri Derron and Fumeng Zhao
Remote Sens. 2025, 17(11), 1900; https://doi.org/10.3390/rs17111900 - 30 May 2025
Cited by 7 | Viewed by 3262
Abstract
Landslide is one of the most frequent and destructive geohazards around the world. The accurate identification of potential landslides plays a vital role in the management of landslide risk. The use of unmanned aerial vehicle (UAV) techniques has recently gained much popularity in [...] Read more.
Landslide is one of the most frequent and destructive geohazards around the world. The accurate identification of potential landslides plays a vital role in the management of landslide risk. The use of unmanned aerial vehicle (UAV) techniques has recently gained much popularity in landslide assessment; however, most of the current UAV-image-based landslide identifications rely upon visual inspections. In this paper, an image-analysis-based landslide identification framework is developed to detect the landslides in UAV images by recognizing the landslide boundaries and ground surface cracks. In this framework, object-oriented image analysis is undertaken to identify the potential landslide boundaries in the input UAV images and the ground surface cracks in the UAV images are recognized by an automatic ground surface crack recognition model, which is trained through a deep transfer learning strategy. With the aid of this transfer learning strategy, the crack recognition model trained can take advantage of the feature of local ground surface cracks in the concerned area and the crack recognition model that has well been developed based on the samples of ground surface cracks collected from different landslide sites. Then, the landslide boundaries and the ground surface cracks obtained are fused based on Boolean operations; the fusion results can allow for informed landslide identification in UAV Images. To illustrate the effectiveness of the proposed image-analysis-based landslide identification framework, the Heifangtai Terrace of Gansu, China, was selected as a study area, and the identification results are further validated through comparisons with the field survey results. Full article
(This article belongs to the Special Issue Artificial Intelligence and Remote Sensing for Geohazards)
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16 pages, 3645 KB  
Article
A Global Coseismic InSAR Dataset for Deep Learning: Automated Construction from Sentinel-1 Observations (2015–2024)
by Xu Liu, Zhenjie Wang, Yingfeng Zhang, Xinjian Shan and Ziwei Liu
Remote Sens. 2025, 17(11), 1832; https://doi.org/10.3390/rs17111832 - 23 May 2025
Cited by 3 | Viewed by 3227
Abstract
Interferometric synthetic aperture radar (InSAR) technology has been widely employed in the rapid monitoring of earthquakes and associated geological hazards. With the continued advancement of InSAR technology, the growing volume of satellite-acquired data has opened new avenues for applying deep learning (DL) techniques [...] Read more.
Interferometric synthetic aperture radar (InSAR) technology has been widely employed in the rapid monitoring of earthquakes and associated geological hazards. With the continued advancement of InSAR technology, the growing volume of satellite-acquired data has opened new avenues for applying deep learning (DL) techniques to the analysis of earthquake-induced surface deformation. Although DL holds great promise for processing InSAR data, its development progress has been significantly constrained by the absence of large-scale, accurately annotated datasets related to earthquake-induced deformation. To address this limitation, we propose an automated method for constructing deep learning training datasets by integrating the Global Centroid Moment Tensor (GCMT) earthquake catalog with Sentinel-1 InSAR observations. This approach reduces the inefficiencies and manual labor typically involved in InSAR data preparation, thereby significantly enhancing the efficiency and automation of constructing deep learning datasets for coseismic deformation. Using this method, we developed and publicly released a large-scale training dataset consisting of coseismic InSAR samples. The dataset contained 353 Sentinel-1 interferograms corresponding to 62 global earthquakes that occurred between 2015 and 2024. Following standardized preprocessing and data augmentation (DA), a large number of image samples were generated for model training. Multidimensional analyses of the dataset confirmed its high quality and strong representativeness, making it a valuable asset for deep learning research on coseismic deformation. The dataset construction process followed a standardized and reproducible workflow, ensuring objectivity and consistency throughout data generation. As additional coseismic InSAR observations become available, the dataset can be continuously expanded, evolving into a comprehensive, high-quality, and diverse training resource. It serves as a solid foundation for advancing deep learning applications in the field of InSAR-based coseismic deformation analysis. Full article
(This article belongs to the Special Issue Artificial Intelligence and Remote Sensing for Geohazards)
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