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Advanced Integration of Remote Sensing Techniques with AI on Geo-Environmental Hazards Detection

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

Deadline for manuscript submissions: 31 May 2024 | Viewed by 4574

Special Issue Editors

Dr. Yusen He

E-Mail Website
Guest Editor
Department of Industrial and System Engineering, University of Iowa, Iowa City, IA 52246, USA
Interests: deep learning; statistical modeling; image analysis; time-series analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Shenghua Cui

E-Mail Website
Guest Editor
State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610081, China
Interests: remote sensing; geo-hazard prevention; landslides; rock mechanics
Dr. Huajin Li

E-Mail Website
Guest Editor
School of Architecture and Civil Engineering, Chengdu University, Chengdu 610106, China
Interests: deep learning; geo-mathematics; image analysis; time-series analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Jingren Zhou

E-Mail Website
Guest Editor
College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, China
Interests: geohazard modeling; dynamic environmental modeling; deep learning; microseismic failure analytics

Special Issue Information

Dear Colleagues,

The geo-environment is intensively and greatly affected by the increased human activities in nature. One significant consequence is that active human activities have caused much damage to the geo-environment, leading to a series of geo-hazards including landslides, surface subsidence, and collapse. Those events pose a serious threat and may result in human casualties, property loss, road damage, the destruction of farmland and forest, and failures of communication infrastructure. Thus, protecting the geo-environment has become a pressing issue and quantification of the potential geohazards plays a crucial role during the protection process.

Remote sensing techniques play a crucial role in geo-environmental hazard detection. The major remote sensing data types include optical, thermal, microwave, and laser scanning images.  They are usually collected from airborne and spaceborne platforms which provide numerous valuable data for geo-hazard detection.

In recent years, advanced methods such as deep learning and AI have attracted an enormous amount of attention across both academia and industry. Emerging from traditional statistical learning methods, AI and deep-learning methods enabled us to learn from advanced representations within the dataset and perform end-to-end optimization. AI methods have demonstrated superior performance in a wide variety of fields such as biomedical engineering, energy systems, and computer vision. Nevertheless, applications that integrate remote sensing techniques and AI in geohazards and the geo-environment sector are still limited in relation to the demand. Hence, there is a huge potential to apply AI, deep learning, and other data science technology to extract information from remote sensing images and enhance human understanding of geo-environmental protection and geohazards prevention.

The main aim of this Research Topic is to integrate remote sensing techniques with deep learning and AI to provide more accurate detection of geo-environmental hazards. We invite researchers and experts from all over the globe to submit high-quality, original research papers or comprehensive reviews. The topics of interest include, but are not limited to:

  • Advanced remote sensing techniques in geo-environmental hazards detection;
  • Deep learning and AI technologies in landslides, land subsidence, debris flow, and floods;
  • Numerical simulation of geohazards including landslides, land subsidence, debris flow, and floods;
  • Geo-environmental hazard quantitative/qualitative assessment and mitigation;
  • Remote sensing analysis for landslides, collapse, soil loss, and other geohazards;
  • Time-series analysis of sensor data for geohazard monitoring such as floods, debris flow, and landslides;
  • Indoor laboratory physical modeling of geohazards including landslides and debris flow;
  • Spatial-temporal analysis of geo-environmental hazards with GIS;

We would like to invite scholars to submit contributions related to the application of remote sensing techniques in various types of geo-environmental hazards including:

  • Landslides;
  • Land subsidence;
  • Collapse;
  • Debris flow;
  • Earthquakes;
  • Groundwater degradation.

Dr. Yusen He
Dr. Shenghua Cui
Dr. Huajin Li
Dr. Jingren Zhou
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 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • remote sensing
  • geo-environmental hazards
  • modeling
  • AI
  • deep learning
  • remote sensing
  • landslides
  • debris
  • collapse
  • earthquakes
  • GIS
  • debris flows
  • numerical simulation
  • statistical modeling

Published Papers (4 papers)

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Research

25 pages, 47432 KiB  
Article
Research on Deformation Evolution of a Large Toppling Based on Comprehensive Remote Sensing Interpretation and Real-Time Monitoring
by Shenghua Cui, Hui Wang, Xiangjun Pei, Luguang Luo, Bin Zeng and Tao Jiang
Remote Sens. 2023, 15(23), 5596; https://doi.org/10.3390/rs15235596 - 01 Dec 2023
Viewed by 835
Abstract
Deep, unstable slopes are highly developed in mountainous areas, especially in the Minjiang River Basin, Sichuan Province, China. In this study, to reveal their deformation evolution characteristics for stability evaluation and disaster prevention, multi-period optical remote sensing images (2010–2019), SBAS-InSAR data (January 2018–December [...] Read more.
Deep, unstable slopes are highly developed in mountainous areas, especially in the Minjiang River Basin, Sichuan Province, China. In this study, to reveal their deformation evolution characteristics for stability evaluation and disaster prevention, multi-period optical remote sensing images (2010–2019), SBAS-InSAR data (January 2018–December 2019), and on-site real-time monitoring (December 2017–September 2020) were utilized to monitor the deformation of a large deep-seated toppling, named the Tizicao (TZC) Toppling. The obtained results by different techniques were cross-validated and synthesized in order to introduce the spatial and temporal characteristics of the toppling. It was found that the displacements on the north side of the toppling are much larger than those on the south side, and the leading edge exhibits a composite damage pattern of “collapse failure” and “bulging cracking”. The development process of the toppling from the formation of a tensile crack at the northern leading edge to the gradual pulling of the rear edge was revealed for a time span of up to ten years. In addition, the correlation between rainfall, earthquakes, and GNSS time series showed that the deformation of the toppling is sensitive to rainfall but does not change under the effect of earthquakes. The surface-displacement-monitoring method in this study can provide a reference for the evolution analysis of unstable slopes with a large span of deformation. Full article
(This article belongs to the Special Issue Advanced Integration of Remote Sensing Techniques with AI on Geo-Environmental Hazards Detection)
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23 pages, 14244 KiB  
Article
Dual Path Attention Network (DPANet) for Intelligent Identification of Wenchuan Landslides
by Xiao Wang, Di Wang, Tiegang Sun, Jianhui Dong, Luting Xu, Weile Li, Shaoda Li, Peilian Ran, Jinxi Ao, Yulan Zou, Jing Wang and Xinnian Zeng
Remote Sens. 2023, 15(21), 5213; https://doi.org/10.3390/rs15215213 - 02 Nov 2023
Cited by 1 | Viewed by 965
Abstract
Currently, the application of remote sensing technology in landslide identification and investigation is an important research direction in the field of landslides. To address the errors arising from the inaccurate extraction of texture and location information in landslide intelligent recognition, we developed a [...] Read more.
Currently, the application of remote sensing technology in landslide identification and investigation is an important research direction in the field of landslides. To address the errors arising from the inaccurate extraction of texture and location information in landslide intelligent recognition, we developed a new network, the dual path attention network (DPANet), and performed experiments in a typical alpine canyon area (Wenchuan County). The results show that the new network recognizes landslide areas with an overall accuracy (OA) and pixel accuracy (PA) of 0.93 and 0.87, respectively, constituting an overall improvement of 4% and 18% compared to the base pyramid scene parsing network (PSPNet). We applied our knowledge of the landslide image features to other areas in the upper reaches of the Minjiang River to enrich the landslide database for this region. Our evaluation of the results shows that the proposed network framework has good robustness and can accurately identify some complex landslides, providing an excellent contribution to the intelligent recognition of landslides. Full article
(This article belongs to the Special Issue Advanced Integration of Remote Sensing Techniques with AI on Geo-Environmental Hazards Detection)
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27 pages, 56971 KiB  
Article
Co-Seismic Landslides Triggered by the 2014 Mw 6.2 Ludian Earthquake, Yunnan, China: Spatial Distribution, Directional Effect, and Controlling Factors
by Yuying Duan, Jing Luo, Xiangjun Pei and Zhuo Liu
Remote Sens. 2023, 15(18), 4444; https://doi.org/10.3390/rs15184444 - 09 Sep 2023
Viewed by 844
Abstract
The 2014 Mw 6.2 Ludian earthquake exhibited a structurally complex source rupture process and an unusual spatial distribution pattern of co-seismic landslides. In this study, we constructed a spatial database consisting of 1470 co-seismic landslides, each exceeding 500 m2. These landslides [...] Read more.
The 2014 Mw 6.2 Ludian earthquake exhibited a structurally complex source rupture process and an unusual spatial distribution pattern of co-seismic landslides. In this study, we constructed a spatial database consisting of 1470 co-seismic landslides, each exceeding 500 m2. These landslides covered a total area of 8.43 km2 and were identified through a comprehensive interpretation of high-resolution satellite images taken before and after the earthquake. It is noteworthy that the co-seismic landslides do not exhibit a linear concentration along the seismogenic fault; instead, they predominantly extend along major river systems with an NE–SW trend. Moreover, the southwest-facing slopes have the highest landslide area ratio of 1.41. To evaluate the susceptibility of the Ludian earthquake-triggered landslides, we performed a random forest model that considered topographic factors (elevation, slope, aspect, distance to rivers), geological factors (lithology), and seismic factors (ground motion parameters, epicentral distance, distance to the seismogenic fault). Our analysis revealed that the distance to rivers and elevation were the primary factors influencing the spatial distribution of the Ludian earthquake-triggered landslides. When we considered the directional variation in ground motion parameters, the AUC of the model slightly decreased. However, incorporating this variation led to a significant reduction in the proportion of areas classified as “high” and “very high” landslide susceptibility. Moreover, SEDd emerged as the most effective ground motion parameter for interpreting the distribution of the co-seismic landslides when compared to PGAd, PGVd, and Iad. Full article
(This article belongs to the Special Issue Advanced Integration of Remote Sensing Techniques with AI on Geo-Environmental Hazards Detection)
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25 pages, 6541 KiB  
Article
Landslide Identification Method Based on the FKGRNet Model for Remote Sensing Images
by Bing Xu, Chunju Zhang, Wencong Liu, Jianwei Huang, Yujiao Su, Yucheng Yang, Weijie Jiang and Wenhao Sun
Remote Sens. 2023, 15(13), 3407; https://doi.org/10.3390/rs15133407 - 05 Jul 2023
Cited by 1 | Viewed by 1236
Abstract
Currently, researchers commonly use convolutional neural network (CNN) models for landslide remote sensing image recognition. However, with the increase in landslide monitoring data, the available multimodal landslide data contain rich feature information, and existing landslide recognition models have difficulty utilizing such data. A [...] Read more.
Currently, researchers commonly use convolutional neural network (CNN) models for landslide remote sensing image recognition. However, with the increase in landslide monitoring data, the available multimodal landslide data contain rich feature information, and existing landslide recognition models have difficulty utilizing such data. A knowledge graph is a linguistic network knowledge base capable of storing and describing various entities and their relationships. A landslide knowledge graph is used to manage multimodal landslide data, and by integrating this graph into a landslide image recognition model, the given multimodal landslide data can be fully utilized for landslide identification. In this paper, we combine knowledge and models, introduce the use of landslide knowledge graphs in landslide identification, and propose a landslide identification method for remote sensing images that fuses knowledge graphs and ResNet (FKGRNet). We take the Loess Plateau of China as the study area and test the effect of the fusion model by comparing the baseline model, the fusion model and other deep learning models. The experimental results show that, first, with ResNet34 as the baseline model, the FKGRNet model achieves 95.08% accuracy in landslide recognition, which is better than that of the baseline model and other deep learning models. Second, the FKGRNet model with different network depths has better landslide recognition accuracy than its corresponding baseline model. Third, the FKGRNet model based on feature splicing outperforms the fused feature classifier in terms of both accuracy and F1-score on the landslide recognition task. Therefore, the FKGRNet model can make fuller use of landslide knowledge to accurately recognize landslides in remote sensing images. Full article
(This article belongs to the Special Issue Advanced Integration of Remote Sensing Techniques with AI on Geo-Environmental Hazards Detection)
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