Remote Sensing and Geological Disasters

Topic Information

Dear Colleagues,

Geological disasters pose significant challenges to human engineering activities, such as the development of underground coal, shale gas, and geothermal resources, as well as the construction of tunnels, bridges, and hydropower stations. These activities often involve complex subsurface environments and stress conditions, necessitating a comprehensive exploration of the underlying principles to address geotechnical engineering problems. The integration of remote sensing technology has become vital for monitoring and analyzing geological disasters, offering real-time data and enhanced predictive capabilities.

By considering various influencing factors and geological characteristics, researchers can explore the complex behaviors in these applications. The advent of network information, big data, and intelligent technologies has provided new methods for studying and mitigating geological disasters. Remote sensing technology, in particular, has emerged as a critical tool for understanding geological hazards and improving the effectiveness of mitigation strategies.

We welcome submissions of cutting-edge reviews, scientific problem analyses, engineering case reports, and other papers related to remote sensing and geological disasters. Additionally, we encourage the submission of papers exploring innovative research methods and new engineering solutions in these fields. Topics of interest include, but are not limited to, the following:

• Remote sensing technology and application in rock mass landslides;
• Integration of geospatial data and engineering geology for disaster risk reduction;
• Advanced remote sensing methods for real-time geohazard assessment;
• Applications of remote sensing in hydrological studies and water resource management related to geological disasters;
• Remote sensing in the analysis of soil and land degradation;
• Thermo-hydro-mechanical coupled model for geological structures;
• Methods and theories for assessing geological stability;
• Slope engineering modeling and landslide disaster prediction methods;
• Application technology of intelligence in geological research;
• Rock structure description and mechanical constitutive equations;
• Failure laws, criteria, and mechanisms of geological materials under high in situ stress;
• Damage, crack initiation, and propagation mechanisms of geological materials under coupled multi-field conditions;
• Mechanical properties of soft and hard geological materials and their mechanisms of deformation. This Topic aims to bridge the gap between remote sensing and geological disaster mitigation, promoting interdisciplinary research and innovative solutions. We look forward to your valuable contributions to this exciting and impactful field.

Dr. Gan Feng
Prof. Dr. Qiao Lyu
Prof. Dr. Yunfeng Ge
Prof. Dr. Guoqing Li
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.3	2011	18.4 Days	CHF 2400	Submit
GeoHazards geohazards	-	2.6	2020	19 Days	CHF 1000	Submit
Geosciences geosciences	2.4	5.3	2011	23.5 Days	CHF 1800	Submit
Land land	3.2	4.9	2012	16.9 Days	CHF 2600	Submit
Remote Sensing remotesensing	4.2	8.3	2009	23.9 Days	CHF 2700	Submit

Preprints.org is a multidisciplinary platform offering a preprint service designed to facilitate the early sharing of your research. It supports and empowers your research journey from the very beginning.

MDPI Topics is collaborating with Preprints.org and has established a direct connection between MDPI journals and the platform. Authors are encouraged to take advantage of this opportunity by posting their preprints at Preprints.org prior to publication:

1. Share your research immediately: disseminate your ideas prior to publication and establish priority for your work.
2. Safeguard your intellectual contribution: Protect your ideas with a time-stamped preprint that serves as proof of your research timeline.
3. Boost visibility and impact: Increase the reach and influence of your research by making it accessible to a global audience.
4. Gain early feedback: Receive valuable input and insights from peers before submitting to a journal.
5. Ensure broad indexing: Web of Science (Preprint Citation Index), Google Scholar, Crossref, SHARE, PrePubMed, Scilit and Europe PMC.

Published Papers (7 papers)

Order results

Content type Publication Date

Result details

Normal Extended Compact

Journals

All Applied Sciences GeoHazards Geosciences Land Remote Sensing

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

Error

Oops... you haven't selected anything for export.

Ok

clear

15 pages, 10610 KiB  

Open AccessArticle

Geological Hazard Risk Assessment Based on Time-Series InSAR Deformation: A Case Study of Xiaojin County, China

by Jiancun Li, Zhao Yan, Liqiang Tong, Yi Wang and Shangyuan Yu

Appl. Sci. 2025, 15(8), 4143; https://doi.org/10.3390/app15084143 - 9 Apr 2025

Viewed by 193

Abstract

Geological hazard risk assessment provides essential scientific support for geological disaster prevention and governance. The selection of appropriate evaluation factors is crucial to the accuracy and practicality of the risk assessment results. The existing factors for geological hazard risk assessment often suffer from [...] Read more.

Geological hazard risk assessment provides essential scientific support for geological disaster prevention and governance. The selection of appropriate evaluation factors is crucial to the accuracy and practicality of the risk assessment results. The existing factors for geological hazard risk assessment often suffer from issues such as poor timeliness and insufficient completeness. Interferometric Synthetic Aperture Radar (InSAR) technology, which offers large-scale, high spatiotemporal resolution monitoring of surface deformation, can effectively compensate for the shortcomings of existing risk assessment factors. How to effectively integrate time-series InSAR deformation results into geological hazard risk assessment has become a focus of research. This study fully considers the time-series InSAR deformation information; both the ascending and descending orbit results of the time-series InSAR deformation are introduced as two categories of evaluation factors in the risk assessment model. Subsequently, 11 types of assessment factors are selected by the Pearson correlation coefficient method, while the Information Volume Model and Evidence Weight Model are applied in the partitioning and assessment of risks in Xiaojin County, China. Finally, ROC (Receiver Operating Characteristic Curve) analysis is utilized to compare the accuracy of model evaluations before and after incorporating time-series InSAR deformation results. The results indicate that: (1) after incorporating time-series InSAR deformation monitoring results as evaluation factors into the information volume model and evidence weight model, the evaluation accuracy of the two models improved by 9.69% and 11.26%, respectively; (2) there are differences in risk partitioning among different evaluation models. From the risk partitioning result of Xiaojin County in this study, the evaluation accuracy of the information volume model is higher than that of the evidence weight model, and the performance is more prominent after adding the time-series InSAR deformation results. Full article

(This article belongs to the Topic Remote Sensing and Geological Disasters)

►▼ Show Figures

Figure 1

22 pages, 5387 KiB  

Open AccessArticle

Landslide Segmentation in High-Resolution Remote Sensing Images: The Van–UPerAttnSeg Framework with Multi-Scale Feature Enhancement

by Chang Li, Quan Zou, Guoqing Li and Wenyang Yu

Remote Sens. 2025, 17(7), 1265; https://doi.org/10.3390/rs17071265 - 2 Apr 2025

Viewed by 279

Abstract

Among geological disasters, landslides are a common and extremely destructive disaster. Their rapid identification is crucial for disaster analysis and response. However, traditional methods of landslide recognition mainly rely on visual interpretation and manual recognition of remote sensing images, which are time-consuming and [...] Read more.

Among geological disasters, landslides are a common and extremely destructive disaster. Their rapid identification is crucial for disaster analysis and response. However, traditional methods of landslide recognition mainly rely on visual interpretation and manual recognition of remote sensing images, which are time-consuming and susceptible to subjective factors, thereby limiting the accuracy and efficiency of recognition. To overcome these limitations, for high-resolution remote sensing images, this method first uses online equalization sampling and enhancement strategy to sample high-resolution remote sensing images to ensure data balance and diversity. Then, it adopts an encoder–decoder structure, where the encoder is a visual attention network (Van) that focuses on extracting discriminative features of different scales from landslide images. The decoder consists of a pyramid pooling module (PPM) and feature pyramid network (FPN), combined with a convolutional block attention module (CBAM) module. Through this structure, the model can effectively integrate features of different scales, achieving precise positioning and recognition of landslide areas. In addition, this study introduces a sliding window algorithm based on Gaussian fusion as a post-processing method, which optimizes the prediction of landslide edge in high-resolution remote sensing images and ensures the context reasoning ability of the model. In the validation set, this method achieved a significant landslide recognition effect with a Dice score of 84.75%, demonstrating high accuracy and efficiency. This result demonstrates the importance and effectiveness of the research method in improving the accuracy and efficiency of landslide recognition, providing strong technical support for analysis and response to geological disasters. Full article

(This article belongs to the Topic Remote Sensing and Geological Disasters)

►▼ Show Figures

Figure 1

23 pages, 28011 KiB  

Open AccessArticle

Predicting the Spatial Distribution of Geological Hazards in Southern Sichuan, China, Using Machine Learning and ArcGIS

by Ruizhi Zhang, Dayong Zhang, Bo Shu and Yang Chen

Land 2025, 14(3), 577; https://doi.org/10.3390/land14030577 - 10 Mar 2025

Viewed by 573

Abstract

Geological hazards in Southern Sichuan have become increasingly frequent, posing severe risks to local communities and infrastructure. This study aims to predict the spatial distribution of potential geological hazards using machine learning models and ArcGIS-based spatial analysis. A dataset comprising 2700 known geological [...] Read more.

Geological hazards in Southern Sichuan have become increasingly frequent, posing severe risks to local communities and infrastructure. This study aims to predict the spatial distribution of potential geological hazards using machine learning models and ArcGIS-based spatial analysis. A dataset comprising 2700 known geological hazard locations in Yibin City was analyzed to extract key environmental and topographic features influencing hazard susceptibility. Several machine learning models were evaluated, including random forest, XGBoost, and CatBoost, with model optimization performed using the Sparrow Search Algorithm (SSA) to enhance prediction accuracy. This study produced high-resolution susceptibility maps identifying high-risk zones, revealing a distinct spatial pattern characterized by a concentration of hazards in mountainous areas such as Pingshan County, Junlian County, and Gong County, while plains exhibited a relatively lower risk. Among different hazard types, landslides were found to be the most prevalent. The results further indicate a strong spatial overlap between predicted high-risk zones and existing rural settlements, highlighting the challenges of hazard resilience in these areas. This research provides a refined methodological framework for integrating machine learning and geospatial analysis in hazard prediction. The findings offer valuable insights for rural land use planning and hazard mitigation strategies, emphasizing the necessity of adopting a “small aggregations and multi-point placement” approach to settlement planning in Southern Sichuan’s mountainous regions. Full article

(This article belongs to the Topic Remote Sensing and Geological Disasters)

►▼ Show Figures

Figure 1

15 pages, 6796 KiB  

Open AccessArticle

A Micro-Topography Enhancement Method for DEMs: Advancing Geological Hazard Identification

by Qiulin He, Xiujun Dong, Haoliang Li, Bo Deng and Jingsong Sima

Remote Sens. 2025, 17(5), 920; https://doi.org/10.3390/rs17050920 - 5 Mar 2025

Viewed by 583

Abstract

Geological hazards in densely vegetated mountainous regions are challenging to detect due to terrain concealment and the limitations of traditional visualization methods. This study introduces the LiDAR image highlighting algorithm (LIHA), a novel approach for enhancing micro-topographical features in digital elevation models (DEMs) [...] Read more.

Geological hazards in densely vegetated mountainous regions are challenging to detect due to terrain concealment and the limitations of traditional visualization methods. This study introduces the LiDAR image highlighting algorithm (LIHA), a novel approach for enhancing micro-topographical features in digital elevation models (DEMs) derived from airborne LiDAR data. By analogizing terrain profiles to non-stationary spectral signals, LIHA applies locally estimated scatterplot smoothing (Loess smoothing), wavelet decomposition, and high-frequency component amplification to emphasize subtle features such as landslide boundaries, cracks, and gullies. The algorithm was validated using the Mengu landslide case study, where edge detection analysis revealed a 20-fold increase in identified micro-topographical features (from 1907 to 37,452) after enhancement. Quantitative evaluation demonstrated LIHA’s effectiveness in improving both human interpretation and automated detection accuracy. The results highlight LIHA’s potential to advance early geological hazard identification and mitigation, particularly when integrated with machine learning for future applications. This work bridges signal processing and geospatial analysis, offering a reproducible framework for high-precision terrain feature extraction in complex environments. Full article

(This article belongs to the Topic Remote Sensing and Geological Disasters)

►▼ Show Figures

Figure 1

16 pages, 12273 KiB  

Open AccessArticle

Early Identification of Geological Hazards Along the Power Transmission Line in Weinan Based on SBAS-InSAR

by Bo Shan, Jianguo Qi, Wucheng Tian, Kuanxing Zhu, Tie Jin, Qingkun Yang, Xiguan An, Guang Yang, Qi Hu and Chen Cao

Appl. Sci. 2025, 15(2), 920; https://doi.org/10.3390/app15020920 - 18 Jan 2025

Viewed by 861

Abstract

Landslides and ground subsidence pose significant threats to the successful construction and operation of transmission line projects in the Loess Plateau region. This study aims to explore an accurate early identification method for geological hazards, providing support for the construction and smooth operation [...] Read more.

Landslides and ground subsidence pose significant threats to the successful construction and operation of transmission line projects in the Loess Plateau region. This study aims to explore an accurate early identification method for geological hazards, providing support for the construction and smooth operation of the transmission project along the route from Baishui County, Weinan City, Shaanxi Province to Lantian County, Xi’an City, Shaanxi Province. Small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technology was used to acquire the surface deformation data of the study area from 4 February 2018 to 21 May 2023. The deformation data were spatially analyzed through kernel density analysis, which quickly and intuitively identified 52 potential geological hazard points in the region, including eight landslides and 44 ground subsidence. Detailed field investigations of the hazards confirmed the accuracy of the identification results. A thorough analysis of typical hazards, such as landslide No. 9 and ground subsidence No. 29, revealed severe deformation, posing a threat to the proposed transmission project. This study indicates that combining InSAR, kernel density analysis, and field investigations can accurately and quickly identify geological hazards around transmission lines, providing support for the site selection and implementation of transmission projects. Full article

(This article belongs to the Topic Remote Sensing and Geological Disasters)

►▼ Show Figures

Figure 1

23 pages, 6062 KiB  

Open AccessArticle

MetaTrans-FSTSF: A Transformer-Based Meta-Learning Framework for Few-Shot Time Series Forecasting in Flood Prediction

by Jiange Jiang, Chen Chen, Anna Lackinger, Huimin Li, Wan Li, Qingqi Pei and Schahram Dustdar

Remote Sens. 2025, 17(1), 77; https://doi.org/10.3390/rs17010077 - 28 Dec 2024

Viewed by 1123

Abstract

Time series forecasting, particularly within the Internet of Things (IoT) and hydrological domains, plays a critical role in predicting future events based on historical data, which is essential for strategic decision making. Effective flood forecasting is pivotal for optimal water resource management and [...] Read more.

Time series forecasting, particularly within the Internet of Things (IoT) and hydrological domains, plays a critical role in predicting future events based on historical data, which is essential for strategic decision making. Effective flood forecasting is pivotal for optimal water resource management and for mitigating the adverse impacts of flood events. While deep learning methods have demonstrated exceptional performance in time series prediction through advanced feature extraction and pattern recognition, they encounter significant limitations when applied to scenarios with sparse data, especially in flood forecasting. The scarcity of historical data can severely hinder the generalization capabilities of traditional deep learning models, presenting a notable challenge in practical flood prediction applications. To address this issue, we introduce MetaTrans-FSTSF, a pioneering meta-learning framework that redefines few-shot time series forecasting. By innovatively integrating MAML and Transformer architectures, our framework provides a specialized solution tailored for the unique challenges of flood prediction, including data scarcity and complex temporal patterns. This framework goes beyond standard implementations, delivering significant improvements in predictive accuracy and adaptability. Our approach leverages Model-Agnostic Meta-Learning (MAML) to enable rapid adaptation to new forecasting tasks with minimal historical data. Our inner architecture is a Transformer-based meta-predictor capable of capturing intricate temporal dependencies inherent in flood time series data. Our framework was evaluated using diverse datasets, including a real-world hydrological dataset from a small catchment area in Wuyuan, China, and other benchmark time series datasets. These datasets were preprocessed to align with the meta-learning approach, ensuring their suitability for tasks with limited data availability. Through extensive evaluation, we demonstrate that MetaTrans-FSTSF substantially improves predictive accuracy, achieving a reduction of up to 16%, 19%, and 8% in MAE compared to state-of-the-art methods. This study highlights the efficacy of meta-learning techniques in overcoming the limitations posed by data scarcity and enhancing flood forecasting accuracy where historical data are limited. Full article

(This article belongs to the Topic Remote Sensing and Geological Disasters)

►▼ Show Figures

Figure 1

15 pages, 10808 KiB  

Open AccessArticle

A Strong Noise Reduction Network for Seismic Records

by Tong Shen, Xuan Jiang, Wenzheng Rong, Lei Xu, Xianguo Tuo and Guili Peng

Appl. Sci. 2024, 14(22), 10262; https://doi.org/10.3390/app142210262 - 7 Nov 2024

Viewed by 1234

Abstract

Noise reduction is a critical step in seismic data processing. A novel strong noise reduction network is proposed in this study. The network enhances the U-Net architecture with an improved inception module and coordinate attention (CA) mechanism, suppressing noise and enhancing signal clarity. [...] Read more.

Noise reduction is a critical step in seismic data processing. A novel strong noise reduction network is proposed in this study. The network enhances the U-Net architecture with an improved inception module and coordinate attention (CA) mechanism, suppressing noise and enhancing signal clarity. These enhancements improve the network’s capability to distinguish between signal and noise in the time–frequency domain. We trained and tested our model on the STEAD dataset, which eliminated noise across various frequency bands, improved the signal-to-noise ratio (SNR) of seismic records, and reduced the waveform distortion significantly. Comparative analyses against U-Net, DeepDenoiser, and DnRDB models, using signals with SNRs ranging from −14 dB to 0 dB, demonstrated our model’s superior performance. At the same time, we demonstrated that the Inception Conv Block has a significant impact on the denoising ability of the network. Furthermore, validation using the “Di Ting” dataset and real noisy signals confirmed the model’s generalizability. These results show that the proposed model significantly outperforms the comparative methods in terms of the SNR, correlation coefficient (r), and root mean square error (RMSE), delivering higher-quality seismograms. The enhanced phase-picking accuracy underscores the potential of our approach to advance in geophysics applications. Full article

(This article belongs to the Topic Remote Sensing and Geological Disasters)

►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

 

Error

Oops... you haven't selected anything for export.

Ok

clear

Displaying articles 1-7