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Remote Sensing for Geology and Mapping (Second Edition)
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Remote Sensing for Geology and Mapping (Second Edition)

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: 28 September 2025 | Viewed by 1932

Special Issue Editors

Prof. Dr. Chao Chen

E-Mail Website
Guest Editor
School of Geography Science and Geomatics Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
Interests: remote sensing; environment
Special Issues, Collections and Topics in MDPI journals
Dr. Tao Chen

E-Mail Website
Guest Editor
School of Geophysics and Spatial Information, China University of Geosciences, Wuhan 430074, China
Interests: remote sensing information models; geohazard prediction; ecological environment monitoring; multimodal data fusion
Special Issues, Collections and Topics in MDPI journals
Dr. Yanni Dong

E-Mail Website
Guest Editor
School of Resource and Environment Sciences, Wuhan University, Wuhan, China
Interests: hyperspectral remote sensing image processing; target detection; dimensionality reduction; classification; metric learning; transfer learning; deep learning; lithologic mapping; geological application of remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing is acquiring information from a distance, which plays an important role in geological survey, mapping, and analysis and can be used to investigate geological characteristics without ground activities. By continually and rapidly obtaining information on resources, environments, and disasters and dynamically monitoring large-scale areas, we can accurately assess contamination environment risks, characterize natural and underground resources, predict geological hazards, etc.

The rapid progress of geology, mapping, and remote sensing has provided continuous data for atmospheric, ocean, and land studies at spatial and temporal scales. The International Conference on Geology, Mapping, and Remote Sensing (ICGMRS) has been held successfully five times. With the support and participation of scholars, experts, institutions, and enterprises in geology, mapping, remote sensing, and marine communication, it has positively promoted comprehensive improvements, developments, and applications in the scientific community. It has also become a panoramic platform for the current research and application results worldwide. This year, the 2024 5th International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2024) was held in Wuhan, Hubei, China, on April 12–14, 2024.

This Special Issue, entitled “Remote Sensing for Geology and Mapping (Second Edition)”, is organized based on the first edition's success and aims to select excellent papers both presented at the conference and published outside the conference. With the advancing development of AI, big data, and sensor technology, how to accurately perceive the dynamic information of massive remote sensing data is becoming a more challenging but interesting subject for researchers and engineers. Therefore, the 2nd edition pays more attention to the application of AI DL in remote sensing, geology, and surveying.

We encourage scholars in related fields to share their ideas and insights by submitting their work, and all original research articles and review articles within the scope of this Special Issue are highly welcome. Potential topics include, but are not limited to:

  • Remote sensing and its application;
  • Planetary remote sensing and mapping;
  • Geographic information science;
  • Remote sensing information engineering;
  • Geographic information system;
  • Global navigation satellite system;
  • Satellite navigation;
  • Earth monitoring and mapping;
  • Classification and data mining techniques;
  • Image processing technology;
  • Hyperspectral image processing;
  • Remote sensing data fusion;
  • Global positioning and navigation system;
  • Remote sensing data quality;
  • Analysis Of remote sensing models;
  • Remote sensing technology application;
  • Surveying and mapping;
  • Surveying and mapping;
  • Marine mapping;
  • General measurement;
  • Photogrammetry;
  • Geodetic survey;
  • Hydrological survey;
  • Mine survey;
  • Engineering survey;
  • Gravity measurement;
  • Aerial photogrammetry;
  • Cartography;
  • City brains, smart oceans, and digital Earth;
  • Sensor technology;
  • Mapping technology;
  • Surveying and mapping instruments;
  • Archeological mapping;
  • Geography and geology;
  • Geological applications of remote sensing;
  • Geological information;
  • Drone systems and geological, mapping, and remote sensing applications;
  • Remote sensing applications in geographical environments, geology, geotechnical engineering, geomechanics, geomorphology, mineral and energy resource exploration, etc.;
  • Remote sensing interpretation of geological structure/tectonic evolution.
  • Other related topics;
  • Theories, techniques, and methods related to surveying, mapping, navigation, and oblique photography;
  • Spatial information decision;
  • 3D scene reconstruction;
  • Marine communication;
  • Natural disaster monitoring and emergency management;
  • Sensor system and technology.

Prof. Dr. Chao Chen
Dr. Tao Chen
Dr. Yanni Dong
Guest Editors

Jintao Liang
Guest Editor Assistant
Affiliation: School of Geophysics and Geomatics, China University of Geosciences (Wuhan), Wuhan 430074, China
Email: Liang_wazg@163.com

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

  • artificial intelligence
  • machine learning
  • deep learning
  • remote sensing
  • object detection
  • segmentation
  • agriculture environment
  • classifications

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
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Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (3 papers)

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Research

34 pages, 17448 KiB  
Article
Soil Classification Maps for the Lower Tagus Valley Area, Portugal, Using Seismic, Geological, and Remote Sensing Data
by João Carvalho, Ruben Dias, José Borges, Lídia Quental and Bento Caldeira
Remote Sens. 2025, 17(8), 1376; https://doi.org/10.3390/rs17081376 - 11 Apr 2025
Viewed by 300
Abstract
The Lower Tagus Valley (LTV) region has the highest population density in Portugal, with over 3.7 million people living in the region. It has been struck in the past by several historical earthquakes, which caused significant economic and human losses. For a proper [...] Read more.
The Lower Tagus Valley (LTV) region has the highest population density in Portugal, with over 3.7 million people living in the region. It has been struck in the past by several historical earthquakes, which caused significant economic and human losses. For a proper seismic hazard evaluation, the area needs detailed Vs30 and soil classification maps. Previously available maps are based on proxies, or an insufficient number of velocity measurements followed by coarse geological generalizations. The focus of this work is to significantly improve the available maps. For this purpose, more than 90 new S-wave seismic velocities measurements obtained from seismic refraction and seismic noise measurements, doubling the number used in previously available maps, are used to update available Vs30 and soil classification maps. The data points are also generalized to the available geological maps using local lithostratigraphic studies and, for the first time, satellite images of this area. The results indicate that lithological and thickness changes within each geological formation prevent a simple generalization of geophysical data interpretation based solely on geological mapping. The maps presented here are the first attempt to produce maps at a scale larger than 1:1,000,000 in Portugal, with direct shear wave velocity measurements. A tentative approach to produce more detailed maps using machine learning was also carried out, presenting promising results. This approach may be used in the future to reduce the number of shear wave measurements necessary to produce detailed maps at a finer scale. Full article
(This article belongs to the Special Issue Remote Sensing for Geology and Mapping (Second Edition))
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24 pages, 26594 KiB  
Article
Deep Learning and Hydrological Feature Constraint Strategies for Dam Detection: Global Application to Sentinel-2 Remote Sensing Imagery
by Hongyuan Gu, Yongnian Gao, Yasen Fei, Yongqi Sun and Yanjun Tian
Remote Sens. 2025, 17(7), 1194; https://doi.org/10.3390/rs17071194 - 27 Mar 2025
Viewed by 233
Abstract
Dams are instrumental in flood and drought control, agricultural irrigation, and hydropower generation. Remote sensing imagery enables the detection of dams across extensive areas, thereby supplying valuable data to facilitate effective water resource management. However, existing dam detection methods cannot achieve high-precision and [...] Read more.
Dams are instrumental in flood and drought control, agricultural irrigation, and hydropower generation. Remote sensing imagery enables the detection of dams across extensive areas, thereby supplying valuable data to facilitate effective water resource management. However, existing dam detection methods cannot achieve high-precision and rapid detection of dams in medium-resolution remote sensing images at the global scale. To fill the gap, deep learning and hydrological feature constraint strategies (DL-HFCS) for dam detection in Sentinel-2 MSI imagery were proposed. This method leverages the efficient YOLOv5s model for preliminary deep learning-based dam detection. Next, based on the hydrological features of dams, constraints such as adjacent water body, single reservoir-based dam number, watershed river network, and detection box-based river network elevation difference are progressively introduced to eliminate false detections. To verify the effectiveness and generalization of our method, 91 1° × 1° regions worldwide were selected as test areas to conduct dam prediction experiments. Experimental results demonstrate that the DL-HFCS achieves a precision of 86.29% and a recall of 82.26%, a 47.58% improvement in precision compared to deep learning alone. Furthermore, over 98% of the detection results accurately locate the dam bodies, whereas in existing dam datasets, this proportion is less than 75%. This study indicates that the HFCS can effectively reduce the false alarm in dam detection. The DL-HFCS method enables thorough and accurate dam detection on a global scale. It holds significant potential for application to Sentinel-2 MSI imagery worldwide, thereby facilitating the creation of a global dam dataset. Full article
(This article belongs to the Special Issue Remote Sensing for Geology and Mapping (Second Edition))
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19 pages, 7702 KiB  
Article
A Novel Method for Simplifying the Distribution Envelope of Green Tide for Fast Drift Prediction in the Yellow Sea, China
by Yi Ding, Song Gao, Guoman Huang, Lingjuan Wu, Zhiyong Wang, Chao Yuan and Zhigang Yu
Remote Sens. 2024, 16(18), 3520; https://doi.org/10.3390/rs16183520 - 23 Sep 2024
Cited by 3 | Viewed by 922
Abstract
Since 2008, annual outbreaks of green tides in the Yellow Sea have had severe impacts on tourism, fisheries, water sports, and marine ecology, necessitating effective interception and removal measures. Satellite remote sensing has emerged as a promising tool for monitoring large-scale green tides [...] Read more.
Since 2008, annual outbreaks of green tides in the Yellow Sea have had severe impacts on tourism, fisheries, water sports, and marine ecology, necessitating effective interception and removal measures. Satellite remote sensing has emerged as a promising tool for monitoring large-scale green tides due to its wide coverage and instantaneous imaging capabilities. Additionally, drift prediction techniques can forecast the location of future green tides based on remote sensing monitoring information. This monitoring and prediction information is crucial for developing an effective plan to intercept and remove green tides. One key aspect of this monitoring information is the green tide distribution envelope, which can be generated automatically and quickly using buffer analysis methods. However, this method produces a large number of envelope vertices, resulting in significant computational burden during prediction calculations. To address this issue, this paper proposes a simplification method based on azimuth difference and side length (SM-ADSL). Compared to the isometric and Douglas–Peucker methods with the same simplification rate, SM-ADSL exhibits better performance in preserving shape and area. The simplified distribution envelope can shorten prediction times and enhance the efficiency of emergency decision-making for green tide disasters. Full article
(This article belongs to the Special Issue Remote Sensing for Geology and Mapping (Second Edition))
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