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Deep Learning and Multi-modal Data Processing for Geological Environment Remote Sensing Interpretation: Methods, Techniques and Applications

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: 30 October 2024 | Viewed by 4048

Special Issue Editors

Dr. Wei Han

E-Mail Website
Guest Editor
School of Computer Science, China University of Geosciences (Wuhan), Wuhan 430074, China
Interests: geological remote sensing interpreting; high-performance computing; deep learning
Dr. Jining Yan

E-Mail Website
Guest Editor
School of Computer Science, China University of Geosciences (Wuhan), Wuhan 430074, China
Interests: time-series analysis; remote sensing; data management and processing; cloud computing
Special Issues, Collections and Topics in MDPI journals
Dr. Xiaohui Huang

E-Mail Website
Guest Editor
School of Computer Science, China University of Geosciences (Wuhan), Wuhan 430074, China
Interests: data management; distributed computing; high-performance geo-computing
Prof. Dr. Yi Wang

E-Mail Website
Guest Editor
Institute of Geophysics & Geomatics, China University of Geosciences, Wuhan 430074, China
Interests: remote sensing image processing; deep learning; geological data intelligent learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The geological environment encompasses the shallow lithosphere and the Earth’s surface, which contains rocks, minerals, glaciers, structures, and other elements, providing essential land, water, and mineral resources for societal and industrial development. In recent years, the rapid growth of multi-source remote sensing imagery, ground monitoring, and geological survey data has provided multi-level and multi-perspective information on the geological environment. Deep learning techniques have showcased remarkable capabilities across various domains, including remote sensing, computer vision, and data processing. Integrating deep learning with multi-modal remote sensing data enhances our ability to understand and interpret elements of the geological environment for high-precision resource exploration, environmental monitoring, and natural disaster prediction, among other applications.

However, in real-world scenarios, the geological environment elements are numerous and fragmented, with homogenization of features, blurred boundaries, and susceptibility to the limitations of remote sensing imaging quality and complex backgrounds, posing considerable challenges to interpreting the category of the geological environment elements efficiently and accurately. Understanding the synergies between deep learning and multi-modal data processing is essential for unlocking new possibilities in geological environment data analysis and applications. Therefore, this Special Issue is dedicated to exploring innovative deep learning methods and their applications within geological environment remote sensing data processing.

Dr. Wei Han
Dr. Jining Yan
Dr. Xiaohui Huang
Prof. Dr. Yi Wang
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

  • novel data sources and deep learning methods for the geological environment, marine, and urban element interpretation
  • multi-source and multi-modal remote sensing data fusion
  • enhancing and denoising geological images using deep learning techniques
  • deep learning applications in monitoring geological disasters, surveys, mineral resources, and other elements
  • deep learning for land cover change analysis
  • cutting-edge techniques for efficient deep learning-based geological processing in distributed environment

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Published Papers (4 papers)

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Research

23 pages, 22622 KiB  
Article
CMFPNet: A Cross-Modal Multidimensional Frequency Perception Network for Extracting Offshore Aquaculture Areas from MSI and SAR Images
by Haomiao Yu, Fangxiong Wang, Yingzi Hou, Junfu Wang, Jianfeng Zhu and Zhenqi Cui
Remote Sens. 2024, 16(15), 2825; https://doi.org/10.3390/rs16152825 - 1 Aug 2024
Viewed by 767
Abstract
The accurate extraction and monitoring of offshore aquaculture areas are crucial for the marine economy, environmental management, and sustainable development. Existing methods relying on unimodal remote sensing images are limited by natural conditions and sensor characteristics. To address this issue, we integrated multispectral [...] Read more.
The accurate extraction and monitoring of offshore aquaculture areas are crucial for the marine economy, environmental management, and sustainable development. Existing methods relying on unimodal remote sensing images are limited by natural conditions and sensor characteristics. To address this issue, we integrated multispectral imaging (MSI) and synthetic aperture radar imaging (SAR) to overcome the limitations of single-modal images. We propose a cross-modal multidimensional frequency perception network (CMFPNet) to enhance classification and extraction accuracy. CMFPNet includes a local–global perception block (LGPB) for combining local and global semantic information and a multidimensional adaptive frequency filtering attention block (MAFFAB) that dynamically filters frequency-domain information that is beneficial for aquaculture area recognition. We constructed six typical offshore aquaculture datasets and compared CMFPNet with other models. The quantitative results showed that CMFPNet outperformed the existing methods in terms of classifying and extracting floating raft aquaculture (FRA) and cage aquaculture (CA), achieving mean intersection over union (mIoU), mean F1 score (mF1), and mean Kappa coefficient (mKappa) values of 87.66%, 93.41%, and 92.59%, respectively. Moreover, CMFPNet has low model complexity and successfully achieves a good balance between performance and the number of required parameters. Qualitative results indicate significant reductions in missed detections, false detections, and adhesion phenomena. Overall, CMFPNet demonstrates great potential for accurately extracting large-scale offshore aquaculture areas, providing effective data support for marine planning and environmental protection. Our code is available at Data Availability Statement section. Full article
(This article belongs to the Special Issue Deep Learning and Multi-modal Data Processing for Geological Environment Remote Sensing Interpretation: Methods, Techniques and Applications)
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19 pages, 14984 KiB  
Article
RSWFormer: A Multi-Scale Fusion Network from Local to Global with Multiple Stages for Regional Geological Mapping
by Sipeng Han, Zhipeng Wan, Junfeng Deng, Congyuan Zhang, Xingwu Liu, Tong Zhu and Junli Zhao
Remote Sens. 2024, 16(14), 2548; https://doi.org/10.3390/rs16142548 - 11 Jul 2024
Viewed by 608
Abstract
Geological mapping involves the identification of elements such as rocks, soils, and surface water, which are fundamental tasks in Geological Environment Remote Sensing (GERS) interpretation. High-precision intelligent interpretation technology can not only reduce labor requirements and significantly improve the efficiency of geological mapping [...] Read more.
Geological mapping involves the identification of elements such as rocks, soils, and surface water, which are fundamental tasks in Geological Environment Remote Sensing (GERS) interpretation. High-precision intelligent interpretation technology can not only reduce labor requirements and significantly improve the efficiency of geological mapping but also assist geological disaster prevention assessment and resource exploration. However, the high interclass similarity, high intraclass variability, gradational boundaries, and complex distributional characteristics of GERS elements coupled with the difficulty of manual labeling and the interference of imaging noise, all limit the accuracy of DL-based methods in wide-area GERS interpretation. We propose a Transformer-based multi-stage and multi-scale fusion network, RSWFormer (Rock–Soil–Water Network with Transformer), for geological mapping of spatially large areas. RSWFormer first uses a Multi-stage Geosemantic Hierarchical Sampling (MGHS) module to extract geological information and high-dimensional features at different scales from local to global, and then uses a Multi-scale Geological Context Enhancement (MGCE) module to fuse geological semantic information at different scales to enhance the understanding of contextual semantics. The cascade of the two modules is designed to enhance the interpretation and performance of GERS elements in geologically complex areas. The high mountainous and hilly areas located in western China were selected as the research area. A multi-source geological remote sensing dataset containing diverse GERS feature categories and complex lithological characteristics, Multi-GL9, is constructed to fill the significant gaps in the datasets required for extensive GERS. Using overall accuracy as the evaluation index, RSWFormer achieves 92.15% and 80.23% on the Gaofen-2 and Landsat-8 datasets, respectively, surpassing existing methods. Experiments show that RSWFormer has excellent performance and wide applicability in geological mapping tasks. Full article
(This article belongs to the Special Issue Deep Learning and Multi-modal Data Processing for Geological Environment Remote Sensing Interpretation: Methods, Techniques and Applications)
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19 pages, 4365 KiB  
Article
Remote Sensing Thematic Product Generation for Sustainable Development of the Geological Environment
by Jiabao Li, Wei Ding, Wei Han, Xiaohui Huang, Ao Long and Yuewei Wang
Remote Sens. 2024, 16(14), 2529; https://doi.org/10.3390/rs16142529 - 10 Jul 2024
Viewed by 618
Abstract
Remote sensing thematic data products are critical for assessing and analyzing geological environments, while efficient generation of thematic products is also highly significant for achieving corresponding sustainable development goals (SDGs). Currently, remote sensing thematic product generation has problems like low levels of automation [...] Read more.
Remote sensing thematic data products are critical for assessing and analyzing geological environments, while efficient generation of thematic products is also highly significant for achieving corresponding sustainable development goals (SDGs). Currently, remote sensing thematic product generation has problems like low levels of automation and efficiency. Addressing these challenges is imperative for advancing sustainable development within the geological environment. This paper aims to address issues related to the generation of geological environment remote sensing thematic products, sorting through the overall process of remote sensing thematic product generation, exploring algorithm encapsulation, combination, and execution under technical methods for container and workflow, and relies on the Spark distributed processing architecture to achieve efficient thematic product generation supported by multiple geological environment data processing models. Finally, taking the three SDGs of SDG6, SDG11, and SDG15 as examples, we achieved the generation of a variety of thematic products such as the interpretation of water body distribution, extraction of urban informal settlements and distribution of water and soil erosion. Meanwhile, we comparatively analyzed the efficiency of thematic product generation on different processing architectures, and the experimental results further verified the feasibility and effectiveness of our proposed solution. This research provides a programme for the automated and intelligent generation of geological environment remote sensing thematic products and effectively assists the construction of sustainable development in the geological environment. Full article
(This article belongs to the Special Issue Deep Learning and Multi-modal Data Processing for Geological Environment Remote Sensing Interpretation: Methods, Techniques and Applications)
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28 pages, 10316 KiB  
Article
Integrating Knowledge Graph and Machine Learning Methods for Landslide Susceptibility Assessment
by Qirui Wu, Zhong Xie, Miao Tian, Qinjun Qiu, Jianguo Chen, Liufeng Tao and Yifan Zhao
Remote Sens. 2024, 16(13), 2399; https://doi.org/10.3390/rs16132399 - 29 Jun 2024
Viewed by 1110
Abstract
The suddenness of landslide disasters often causes significant loss of life and property. Accurate assessment of landslide disaster susceptibility is of great significance in enhancing the ability of accurate disaster prevention. To address the problems of strong subjectivity in the selection of assessment [...] Read more.
The suddenness of landslide disasters often causes significant loss of life and property. Accurate assessment of landslide disaster susceptibility is of great significance in enhancing the ability of accurate disaster prevention. To address the problems of strong subjectivity in the selection of assessment indicators and low efficiency of the assessment process caused by the insufficient application of a priori knowledge in landslide susceptibility assessment, in this paper, we propose a novel landslide susceptibility assessment framework by combing domain knowledge graph and machine learning algorithms. Firstly, we combine unstructured data, extract priori knowledge based on the Unified Structure Generation for Universal Information Extraction Pre-trained model (UIE) fine-tuned with a small amount of labeled data to construct a landslide susceptibility knowledge graph. We use Paired Relation Vectors (PairRE) to characterize the knowledge graph, then construct a target area characterization factor recommendation model by calculating spatial correlation, attribute similarity, Term Frequency–Inverse Document Frequency (TF-IDF) metrics. We select the optimal model and optimal feature combination among six typical machine learning (ML) models to construct interpretable landslide disaster susceptibility assessment mapping. Experimental validation and analysis are carried out on the three gorges area (TGA), and the results show the effectiveness of the feature factors recommended by the knowledge graph characterization learning, with the overall accuracy of the model after adding associated disaster factors reaching 87.2%. The methodology proposed in this research is a better contribution to the knowledge and data-driven assessment of landslide disaster susceptibility. Full article
(This article belongs to the Special Issue Deep Learning and Multi-modal Data Processing for Geological Environment Remote Sensing Interpretation: Methods, Techniques and Applications)
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