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

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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: closed (30 October 2024) | Viewed by 6962

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Special Issue Editors

Dr. Wei Han

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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

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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

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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

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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

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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 (7 papers)

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Research

22 pages, 23252 KiB

Open AccessArticle

Quantifying the Effect of Land Use and Land Cover Changes on Spatial-Temporal Dynamics of Water in Hanjiang River Basin

by Hao Xi, Yanbin Yuan, Heng Dong and Xiaopan Zhang

Remote Sens. 2024, 16(22), 4136; https://doi.org/10.3390/rs16224136 - 6 Nov 2024

Viewed by 707

Abstract

As a vital part of the geo-environment and water cycle, ecosystem health and human development are dependent on water resources. Water supply and demand are influenced significantly by land use and cover change (LUCC) which shapes the surface ecosystems by altering their structure and function. Under future climate change scenarios, LUCC may greatly impact regional water balance, yet the impact is still not well understood. Therefore, examining the spatial relationship between LUCC and water yield services is crucial for optimizing land resources and informing sustainable development policies. In this study, we focused on the Hanjiang River Basin and used the patch-generating land use simulation (PLUS) model, coupled with the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, to assess water yield services under three Shared Socioeconomic Pathway and Representative Concentration Pathway (SSP-RCP) scenarios. For the first time, we considered the impact of future changes in socio-economic and water use indicators on water demand using correction factors and ARIMA projections. The relationship between water supply and demand was explored using this approach, and LUCC’s effects on this balance are also discussed. Results indicate that: (1) The patterns of LUCC are similar for the three scenarios from 2030 to 2050, with varying levels of decrease for cropland and significant growth of built-up areas, with increases of 6.77% to 19.65% (SSP119), 7.66% to 22.65% (SSP245), and 15.88% to 46.69% (SSP585), respectively, in the three scenarios relative to 2020; (2) The future supply and demand trends for the three scenarios of produced water services are similar, and the overall supply and demand risks are all on a downward trend. Water demand continues to decline, and by 2050, the water demand of the 3 scenarios will decrease by

96.275 \times 10^{8} t

81.210 \times 10^{8} t

, and

84.13 \times 10^{8} t

relative to 2020, respectively; while supply decreases from 2030 to 2040 and rises from 2040 to 2050; (3) Both water supply and demand distributions exhibit spatial correlation, and the distribution of hotspots is similar. The water supply and demand are well-matched, with an overall supply-demand ratio greater than 1.5; (4) LUCC can either increase or decrease water yield. Built-up land provides more water supply compared to other land types, while forest land has the lowest average water supply. Limiting land use type conversions can enhance the water supply. 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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22 pages, 46624 KiB

Open AccessArticle

Autonomous Extraction Technology for Aquaculture Ponds in Complex Geological Environments Based on Multispectral Feature Fusion of Medium-Resolution Remote Sensing Imagery

by Zunxun Liang, Fangxiong Wang, Jianfeng Zhu, Peng Li, Fuding Xie and Yifei Zhao

Remote Sens. 2024, 16(22), 4130; https://doi.org/10.3390/rs16224130 - 5 Nov 2024

Viewed by 548

Abstract

Coastal aquaculture plays a crucial role in global food security and the economic development of coastal regions, but it also causes environmental degradation in coastal ecosystems. Therefore, the automation, accurate extraction, and monitoring of coastal aquaculture areas are crucial for the scientific management of coastal ecological zones. This study proposes a novel deep learning- and attention-based median adaptive fusion U-Net (MAFU-Net) procedure aimed at precisely extracting individually separable aquaculture ponds (ISAPs) from medium-resolution remote sensing imagery. Initially, this study analyzes the spectral differences between aquaculture ponds and interfering objects such as saltwater fields in four typical aquaculture areas along the coast of Liaoning Province, China. It innovatively introduces a difference index for saltwater field aquaculture zones (DIAS) and integrates this index as a new band into remote sensing imagery to increase the expressiveness of features. A median augmented adaptive fusion module (MEA-FM), which adaptively selects channel receptive fields at various scales, integrates the information between channels, and captures multiscale spatial information to achieve improved extraction accuracy, is subsequently designed. Experimental and comparative results reveal that the proposed MAFU-Net method achieves an F1 score of 90.67% and an intersection over union (IoU) of 83.93% on the CHN-LN4-ISAPS-9 dataset, outperforming advanced methods such as U-Net, DeepLabV3+, SegNet, PSPNet, SKNet, UPS-Net, and SegFormer. This study’s results provide accurate data support for the scientific management of aquaculture areas, and the proposed MAFU-Net method provides an effective method for semantic segmentation tasks based on medium-resolution remote sensing images. 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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14 pages, 16241 KiB

Open AccessArticle

Seismic Random Noise Attenuation Using DARE U-Net

by Tara P. Banjade, Cong Zhou, Hui Chen, Hongxing Li, Juzhi Deng, Feng Zhou and Rajan Adhikari

Remote Sens. 2024, 16(21), 4051; https://doi.org/10.3390/rs16214051 - 30 Oct 2024

Viewed by 561

Abstract

Seismic data processing plays a pivotal role in extracting valuable subsurface information for various geophysical applications. However, seismic records often suffer from inherent random noise, which obscures meaningful geological features and reduces the reliability of interpretations. In recent years, deep learning methodologies have shown promising results in performing noise attenuation tasks on seismic data. In this research, we propose modifications to the standard U-Net structure by integrating dense and residual connections, which serve as the foundation of our approach named the dense and residual (DARE U-Net) network. Dense connections enhance the receptive field and ensure that information from different scales is considered during the denoising process. Our model implements local residual connections between layers within the encoder, which allows earlier layers to directly connect with deep layers. This promotes the flow of information, allowing the network to utilize filtered and unfiltered input. The combined network mechanisms preserve the spatial information loss during the contraction process so that the decoder can locate the features more accurately by retaining the high-resolution features, enabling precise location in seismic image denoising. We evaluate this adapted architecture by applying synthetic and real data sets and calculating the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). The effectiveness of this method is well noted. 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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23 pages, 22622 KiB

Open AccessArticle

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 946

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 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

Open AccessArticle

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 747

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 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

Open AccessArticle

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 765

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 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

Open AccessArticle

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 1337

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 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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