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Advances in AI-Driven Remote Sensing for Geohazard Perception

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A Special Issue of Remote Sensing (ISSN 2072-4292) belonging to the section "AI Remote Sensing".

Deadline for manuscript submissions: closed (31 August 2026) | Viewed by 8641

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Editors

Prof. Dr. Mingtao Ding

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Guest Editor

School of Geology Engineering and Geomatics, Chang’an University, No.126 Yanta Road, Xi’an 710054, China
Interests: remote sensing; geohazard; computer vision; artificial intelligence

Prof. Dr. Chong Xu

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Guest Editor

National Institute of Natural Hazards, Ministry of Emergency Management of China, Bejing 100085, China
Interests: landslides; GIS; remote sensing; natural hazards
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Prof. Dr. Weile Li

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State Key Laboratory of Geohazard Prevention and Geoenvironment Protection (SKLGP), Chengdu University of Technology, Chengdu 610059, China
Interests: landslide detection; landslide monitoring and early warning; InSAR
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Junchuan Yu

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Guest Editor

China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083, China
Interests: remote sensing; deep learning; geohazard; hyperspectral; visual foundation model

Special Issue Information

Dear Colleagues,

The integration of artificial intelligence (AI) into remote sensing has revolutionized the perception of geohazards—such as landslides, earthquakes, and volcanic eruptions—that pose significant risks to human life and infrastructure. Advancements in remote sensor technologies, including optical imagery and synthetic aperture radar (SAR) imagery, have enhanced the ability to detect and analyze these geohazards. AI algorithms, particularly deep learning techniques and foundational models, have further improved the accuracy and efficiency of interpreting extensive remote sensing datasets, enabling the rapid identification of potential threats and informing disaster response strategies. This interdisciplinary approach not only enhances our cognition of geohazard features, but also contributes to more effective risk assessment and mitigation efforts, ultimately promoting resilience against geohazards.

The aim of this Special Issue is to gather interdisciplinary contributions that push the boundaries of how AI algorithms—ranging from machine learning, deep learning, and foundational models—can be harnessed to extract critical insights from remote sensing datasets. We encourage submissions that not only address technical developments and algorithmic innovations, but also discuss practical implementations and challenges encountered in real-world scenarios. Contributions may include case studies, methodological advancements, theoretical frameworks, and comparative analyses that demonstrate enhancements in the accuracy, timeliness, and reliability of geohazard detection and risk assessment.

This Special Issue invites original research and review articles that explore cutting-edge AI methodologies applied to remote sensing for geohazard perception. The scope of this Special Issue includes, but is not limited to, the following:

AI-based algorithms for the automated feature extraction and pattern recognition of remote sensing data;
AI-driven geohazard mapping, susceptibility mapping, and risk assessment;
Deep learning approaches for the monitoring, prediction, and early warning of geohazard events;
Integration of multi-sensor data (satellite, airborne LiDAR, radar, and UAV imagery) for enhanced geohazard detection, recognition, monitoring, and analysis;
Real-time processing and decision support systems for emergency management.

Prof. Dr. Mingtao Ding
Prof. Dr. Chong Xu
Prof. Dr. Weile Li
Prof. Dr. Junchuan Yu
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-anonymized 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

remote sensing
artificial intelligence
deep learning
foundation models
geohazard
object detection
object recognition
monitoring and warning
risk assessment

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Published Papers (8 papers)

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Research

34 pages, 69553 KB

Open AccessArticle

The Capacity of Generative Models to Synthesize Regional Landslide and Non-Landslide Remote Sensing Imagery Under Data-Scarce Scenarios: Insights from Multimodal Foundation Models

by Yiwei Liu, Ye Tao, Aiguo Xing, Qiankuan Wang and Muhammad Bilal

Remote Sens. 2026, 18(18), 3100; https://doi.org/10.3390/rs18183100 - 9 Sep 2026

Abstract

Landslide interpretation based on remote sensing data is pivotal for efficient emergency response and risk management. However, the scarcity of high-quality landslide data remains a major bottleneck for data-driven landslide analysis. To address this challenge, this study investigates the potential of multimodal foundation models for generating high-quality synthetic landslide and non-landslide remote sensing images. The proposed regional remote sensing image synthesis framework based on Stable Diffusion models and Low-Rank Adaptation enables more controllable and interpretable remote sensing data augmentation under data-scarce scenarios. Based on the publicly available Bijie landslide dataset, landslide and non-landslide remote sensing image–semantic annotation databases can be separately constructed and subsequently utilized to fine-tune text-to-image diffusion models. By conducting comparative experiments across three Stable Diffusion backbones, the performance of the generative models in both landslide and non-landslide scenarios is systematically and quantitatively evaluated. Experimental results demonstrate that LoRA fine-tuning can effectively transfer landslide-specific visual knowledge into diffusion models, enabling the generation of high-fidelity synthetic remote sensing images with texture and structure closely matching real samples. Compared with the StyleGAN2 baseline with a minimum FID of 67.47 in the recent literature, the proposed SDXL-LoRA model achieves superior generation quality with a minimum FID of 54.70. In addition, the study indicates that the optimal diffusion backbone depends on semantic complexity. Accordingly, a heterogeneous backbone strategy should be adopted when constructing balanced synthetic datasets for downstream applications. The training configurations employed in this study also provide a practical reference for related research. This study exploratorily applies multimodal generative foundation models to landslide-related remote sensing data augmentation and provides a flexible and transferable solution for regional geohazard studies. Full article

(This article belongs to the Special Issue Advances in AI-Driven Remote Sensing for Geohazard Perception)

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24 pages, 46925 KB

Open AccessArticle

Multi-Method Identification and Spatiotemporal Evolution Analysis of Active Deformation Areas in the Binchang Mining Area

by Liuru Hu, Chenzhe Wang, Yanan Ji, Jun Deng, Chuang Song, Yaqing Li, Zeyu Zhang, Lin Yu and Chen Yu

Remote Sens. 2026, 18(14), 2339; https://doi.org/10.3390/rs18142339 - 13 Jul 2026

Viewed by 379

Abstract

Mining-induced land subsidence and geohazards have become increasingly prominent in loess mining areas. Accurate identification of Active Deformation Areas (ADAs) is of great significance for mining safety, ecological protection, and geohazard prevention. The study selected the Binchang mining and used SBAS-InSAR based on Sentinel-1 SAR images from 2020 to 2025 to derive cumulative deformation and velocity. Three ADA identification approaches, including ADAfinder, MCAS-TO, and Light-UNet deep learning method, were quantitatively evaluated using IoU, F1-score, Boundary_F1-score, Precision, Recall, Area Error (%), and comparatively analyzed. ADAfinder identifies subsidence centers but produces fragmented results. MCAS-TO improves boundary continuity but is constrained by a 2% area threshold, while the deep learning method achieves the highest boundary detection accuracy and captures ADAs expansion and merging processes. Spatially, the identified ADAs are mainly distributed in the central and southern parts of the mining area and are highly consistent with underground coal mining faces. Comprehensive analysis of the three ADA identification results reveals the spatiotemporal evolution of mining-induced deformation. The results demonstrate that mining activities are the dominant driving factor controlling the formation and evolution of ADAs. The findings can provide important technical support for geohazard prevention in loess mining areas. Full article

(This article belongs to the Special Issue Advances in AI-Driven Remote Sensing for Geohazard Perception)

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25 pages, 7686 KB

Open AccessArticle

LEViM-Net: A Lightweight EfficientViM Network for Earthquake Building Damage Assessment

by Qing Ma, Dongpu Wu, Yichen Zhang, Jiquan Zhang, Jinyuan Xu and Yechi Yao

Remote Sens. 2026, 18(10), 1592; https://doi.org/10.3390/rs18101592 - 15 May 2026

Viewed by 457

Abstract

Building damage and collapse are the main sources of serious casualties and financial losses during earthquakes, which are among the most destructive natural disasters that endanger human life and property. Therefore, quick and precise post-earthquake building damage assessment is essential for risk assessment and emergency action. Convolutional neural networks (CNNs) primarily concentrate on local features and frequently ignore global contextual information within and across buildings, despite the fact that deep learning-based techniques allow automated damage identification. Transformer-based approaches, on the other hand, are good at capturing global dependencies, but their large memory and processing costs restrict their usefulness. As a result, existing networks still struggle to achieve an effective balance between accuracy and efficiency. To address this issue, this study proposes a lightweight and efficient network for post-earthquake building damage assessment. Specifically, we develop a two-stage method based on EfficientViM with an encoder–decoder architecture. In the encoder, Mamba is introduced to extract multi-scale change features with long-range dependencies, leveraging the state space model to preserve global modeling capability while significantly reducing computational complexity. In the decoder, two lightweight modules are designed to further enhance discriminative capability and computational efficiency. The network finally outputs building localization and pixel-level building damage, respectively. Experiments were conducted on four earthquake events from the BRIGHT dataset using a three-for-training and one-for-testing cross-event rotation evaluation strategy. The results demonstrate that LEViM-Net requires only 30.94 M parameters and 27.10 G FLOPs. In addition, for the Türkiye earthquake event, the proposed method achieves an F1 score of 80.49%, an overall accuracy (OA) of 88.17%, and a mean intersection over union (mIoU) of 49.73%. The proposed model enables efficient remote-sensing-based mapping of macroscopic and image-visible building damage, providing timely support for early-stage emergency response. Full article

(This article belongs to the Special Issue Advances in AI-Driven Remote Sensing for Geohazard Perception)

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27 pages, 48488 KB

Open AccessArticle

Landslide Susceptibility Assessment in Tongren County, Qinghai Province, Using Machine Learning and Multi–Source Data Integration: A Comparative Analysis of Models

by Yuanfei Pan, Jianhui Dong, Yangdan Dong, Minggao Tang, Ran Tang, Zhanxi Wei, Xiao Wang and Xinhao Yao

Remote Sens. 2026, 18(10), 1583; https://doi.org/10.3390/rs18101583 - 15 May 2026

Viewed by 669

Abstract

Accurate landslide susceptibility assessment remains challenging in mountainous regions with complex terrain, heterogeneous geology, and clustered landslide inventories. This study develops a slope–unit–based landslide susceptibility assessment framework for Tongren County, Qinghai Province, China, using a landslide inventory of 217 events, multi–source environmental data, Certainty Factor (CF)–based conditioning–factor analysis, and machine learning models. Eighteen conditioning factors derived from remote sensing, geological survey, and meteorological datasets were extracted at the slope–unit scale, and their collinearity was evaluated using Pearson’s correlation and the Variance Inflation Factor (VIF). Eight models—Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), AdaBoost, Decision Tree (DT), XGBoost, K–Nearest Neighbors (KNN), and Convolutional Neural Network (CNN)—were evaluated under a 70:30 train/test split. The results show clear performance differences among the tested models: SVM achieved the best overall balance between discrimination and landslide detection (AUC = 0.9489; recall = 0.879). The tested CNN baseline showed relatively weak performance under the current slope–unit–based tabular–data setting. Susceptibility zoning results showed that high– and very–high–susceptibility zones were mainly concentrated along the Longwu River and its tributaries, where middle–elevation dissected terrain, weak lithological materials, river–valley erosion, and human engineering activities spatially coincide. These results provide a practical basis for slope monitoring and land–use planning in Tongren County. Full article

(This article belongs to the Special Issue Advances in AI-Driven Remote Sensing for Geohazard Perception)

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21 pages, 10378 KB

Open AccessArticle

A Method for Detecting Slow-Moving Landslides Based on the Integration of Surface Deformation and Texture

by Xuerong Chen, Cuiying Zhou, Zhen Liu, Chaoying Zhao, Xiaojie Liu and Zhong Lu

Remote Sens. 2026, 18(6), 899; https://doi.org/10.3390/rs18060899 - 15 Mar 2026

Viewed by 808

Abstract

Slow-moving landslides can trigger severe disasters when activated by earthquakes, torrential rains, or typhoons. Early detection is crucial for mitigating loss of life and property damage. Interferometric Synthetic Aperture Radar (InSAR) technology is among the most effective techniques for detecting slow-moving landslides, though its accuracy can be further improved through integration with optical imagery and Digital Elevation Models (DEM). Current machine learning approaches that combine InSAR and optical data suffer from limited efficiency, poor transferability, and challenges in regional-scale application. To address these limitations, this study proposes a multimodal dual-path network that integrates InSAR products with textural information from optical imagery to detect slow-moving landslides. One path processes InSAR deformation rates and topographic factors, while the other incorporates texture information and auxiliary data. Together, these paths extract semantic information from high-dimensional spatial features and condense it into low-dimensional representations. A pyramid pooling module is employed to capture multi-scale features during low-level semantic extraction. For feature fusion, a rate-constrained adaptive module is introduced to enhance the contribution of deformation rates to slow-moving landslides. According to the results, the proposed method improves the F1-score for landslide detection by 6% compared to using InSAR products alone. These results provide reliable technical support for regional landslide inventory compilation and disaster management, as well as new insights for regional-scale surveys in slow-moving landslide-prone areas. Full article

(This article belongs to the Special Issue Advances in AI-Driven Remote Sensing for Geohazard Perception)

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28 pages, 7496 KB

Open AccessArticle

Spatial Zoning Characteristics of Thaw Settlement in Separated Subgrades in Permafrost Regions of the Qinghai–Tibet Engineering Corridor

by Jianbing Chen, Xiaona Liu, Ming Li, Jinping Li, Pan Chen, Xiang Long, Fuqing Cui and Zhiyun Liu

Remote Sens. 2026, 18(5), 835; https://doi.org/10.3390/rs18050835 - 9 Mar 2026

Viewed by 752

Abstract

Thaw settlement (TS) in warm and ice-rich permafrost presents a challenge to highway subgrade stability in the Qinghai–Tibet Engineering Corridor (QTEC). To conduct a regional risk assessment, this study develops a framework coupling multi-source data fusion with Random Forest (RF) machine learning. By connecting site-specific thermo-mechanical simulations with corridor-scale remote sensing predictors, a 30 m resolution thaw settlement zoning map for 13 m wide separated subgrades was generated. The results indicate the following: (1) Thaw settlement exhibits significant spatial variability, with Level III settlement (20–30 cm) being the dominant category, accounting for 40.85% of the total area; Level IV and V settlements are mainly distributed in warm and ice-rich permafrost regions such as the Chumar River, Wuli, and Tuotuo River areas. (2) Mean annual ground temperature (MAGT) and ice content type (ICT) are key factors influencing the spatial settlement pattern, with differentiated dominant mechanisms: 50% of the zones are dominated by ICT, corresponding to higher settlement (26.76–43.31 cm); 35.71% are influenced by both MAGT and ICT; and 14.29% are dominated by MAGT, with lower settlement (16.23–24.19 cm). This suggests a distinct spatial pattern where “high-temperature zones are largely controlled by ice content, while low-temperature zones are controlled by temperature.” (3) Among multi-source remote sensing factors, land surface temperature (LST) and the thawing index (TI) show significant correlations with thaw settlement, confirming their applicability for hazard identification in high-altitude regions. This study provides a scientific reference and decision support for engineering maintenance and route selection on the Qinghai–Tibet Plateau. Full article

(This article belongs to the Special Issue Advances in AI-Driven Remote Sensing for Geohazard Perception)

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27 pages, 23454 KB

Open AccessArticle

Towards Accurate Prediction of Runout Distance of Rainfall-Induced Shallow Landslides: An Integrated Remote Sensing and Explainable Machine Learning Framework in Southeast China

by Xiaoyu Yi, Yuan Wang, Wenkai Feng, Jiachen Zhao, Zhenghai Xue and Ruijian Huang

Remote Sens. 2025, 17(22), 3660; https://doi.org/10.3390/rs17223660 - 7 Nov 2025

Cited by 6 | Viewed by 1935

Abstract

This study addresses the challenge of predicting runout distance of rainfall-induced shallow landslides by integrating deep learning and explainable machine learning. Using the June 2024 landslide disaster at the Fujian-Guangdong-Jiangxi border as a case study and remote sensing images as the data source, we developed an improved U-Shaped Convolutional Neural Network model (RAC-Unet) combining Deep Residual Structure, Atrous Spatial Pyramid Pooling, and Convolutional Block Attention Module modules. The model identified 34,376 shallow landslides and built a dynamic parameter database with 8875 samples, which was used for data-driven model training. After comparing models, Extreme Gradient Boosting was chosen as the best (R² = 0.923), with its performance confirmed by Wilcoxon analysis and good generalization in external validation (R² = 0.877). SHapley Additive Explanations analysis revealed how factors like the area of the sliding source zone (SA), length/width ratio of the sliding source zone (SLWR), and average slope of the source zone (SS) affect landslide runout, a simplified model using the three parameters SA, SLWR, and SS was constructed (R² = 0.862). Compared to traditional models, this integrated framework solves the pre-disaster impact range estimation problem, deepens understanding of shallow landslide dynamics, and enables accurate pre- and post-disaster predictions. It provides comprehensive support for disaster risk assessment and emergency response in southeastern hilly areas. Full article

(This article belongs to the Special Issue Advances in AI-Driven Remote Sensing for Geohazard Perception)

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27 pages, 17902 KB

Open AccessArticle

Identification of Dominant Controlling Factors and Susceptibility Assessment of Coseismic Landslides Triggered by the 2022 Luding Earthquake

by Jin Wang, Mingdong Zang, Jianbing Peng, Chong Xu, Zhandong Su, Tianhao Liu and Menghao Li

Remote Sens. 2025, 17(16), 2797; https://doi.org/10.3390/rs17162797 - 12 Aug 2025

Cited by 3 | Viewed by 1565

Abstract

Coseismic landslides are geological events in which slopes, either on the verge of instability or already in a fragile state, experience premature failure due to seismic shaking. On 5 September 2022, an Ms 6.8 earthquake struck Luding County, Sichuan Province, China, triggering numerous landslides that caused severe casualties and property damage. This study systematically interprets 13,717 coseismic landslides in the Luding earthquake’s epicentral area, analyzing their spatial distribution concerning various factors, including elevation, slope gradient, slope aspect, plan curvature, profile curvature, surface cutting degree, topographic relief, elevation coefficient variation, lithology, distance to faults, epicentral distance, peak ground acceleration (PGA), distance to rivers, fractional vegetation cover (FVC), and distance to roads. The analytic hierarchy process (AHP) was improved by incorporating frequency ratio (FR) to address the subjectivity inherent in expert scoring for factor weighting. The improved AHP, combined with the Pearson correlation analysis, was used to identify the dominant controlling factor and assess the landslide susceptibility. The accuracy of the model was verified using the area under the receiver operating characteristic (ROC) curve (AUC). The results reveal that 34% of the study area falls into very-high- and high-susceptibility zones, primarily along the Moxi segment of the Xianshuihe fault and both sides of the Dadu river valley. Tianwan, Caoke, Detuo, and Moxi are at particularly high risk of coseismic landslides. The elevation coefficient variation, slope aspect, and slope gradient are identified as the dominant controlling factors for landslide development. The reliability of the proposed model was evaluated by calculating the AUC, yielding a value of 0.8445, demonstrating high reliability. This study advances coseismic landslide susceptibility assessment and provides scientific support for post-earthquake reconstruction in Luding. Beyond academic insight, the findings offer practical guidance for delineating priority zones for risk mitigation, planning targeted engineering interventions, and establishing early warning and monitoring strategies to reduce the potential impacts of future seismic events. Full article

(This article belongs to the Special Issue Advances in AI-Driven Remote Sensing for Geohazard Perception)

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