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Remote Sensing
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Applications of Photogrammetry and Lidar Techniques in Mining Areas
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Applications of Photogrammetry and Lidar Techniques in Mining Areas

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: 31 July 2026 | Viewed by 2366

Special Issue Editors

Dr. Zhihua Xu

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Guest Editor
College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
Interests: photogrammetry and laser scanning
Dr. Zhenghua Zhang

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Guest Editor Assistant
School of Environmental and Spatial Informatics, China University of Mining and Technology, Xuzhou 221100, China
Interests: LiDAR SLAM; point cloud processing; 3D deep learning
Dr. Xiaohu Lin

E-Mail Website
Guest Editor Assistant
School of College of Surveying and Mapping Science and Technology, Xi’an University of Science and Technology, Xi’an 710054, China
Interests: intelligent navigation and positioning of mobile robots; intelligent perception and monitoring

Special Issue Information

Dear Colleagues,

The mining industry is undergoing a significant transformation, driven by the need for enhanced safety, improved efficiency, and greater automation. Central to this evolution are photogrammetry and Light Detection and Ranging (LiDAR) technologies, which have revolutionized how we capture, analyze, and interpret 3D spatial information in complex mining environments. Moving beyond traditional surveying methods, modern photogrammetry (e.g., Structure from Motion) and LiDAR (from terrestrial, mobile, and aerial platforms) provide dense, accurate, and rapid point cloud data for both surface and underground operations. These technologies are foundational for tackling critical challenges, especially in the context of intelligent mine construction. In harsh and GNSS-denied underground environments, such as those in coal mines, LiDAR-based SLAM (Simultaneous Localization and Mapping) and photogrammetry are paramount for autonomous navigation, vehicle positioning, and dynamic environment mapping. The importance of this research area lies in its direct impact on creating safer working conditions, optimizing resource extraction, and enabling the next generation of autonomous mining systems.

This Special Issue aims to bring together the latest research, innovations, and case studies on the application of photogrammetry and LiDAR in the mining sector. We seek to highlight novel techniques, algorithms, and integrated systems that address the unique challenges of acquiring and processing 3D data in both open-pit and subterranean mines. This topic is perfectly aligned with the scope of Remote Sensing, as it focuses on advanced sensor technologies (LiDAR and cameras), data processing methodologies (point cloud analysis, SLAM, and sensor fusion), and their application to earth science and engineering challenges. We hope to foster a collection of high-quality articles that will serve as a benchmark for future research and development in this vital field.

Prospective authors are invited to contribute original research articles, reviews, and case studies on a broad spectrum of topics, including, but not limited to, the following:

  • Photogrammetric and LiDAR-based 3D mapping of underground tunnels, stopes, and caverns;
  • SLAM algorithms for positioning, navigation, and mapping in GPS-denied mining environments;
  • Applications in intelligent coal mine construction and autonomous mining;
  • UAV and terrestrial photogrammetry and LiDAR for open-pit mine mapping;
  • Volumetric analysis of stockpiles, ore passes, and excavation progress;
  • Deformation analysis, convergence monitoring, and slope stability assessment;
  • Sensor fusion of LiDAR, photogrammetry, IMU, and other geodetic sensors;
  • Development and validation of point cloud processing algorithms for mining applications (e.g., change detection, segmentation, and ecological monitoring);
  • Creation of digital twins for mining operations;
  • Challenges and future directions for 3D remote sensing in the mining industry.

Dr. Zhihua Xu
Guest Editor

Dr. Zhenghua Zhang
Dr. Xiaohu Lin
Guest Editor Assistants

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 250 words) can be sent to the Editorial Office for assessment.

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

  • photogrammetry
  • laser scanning
  • mining engineering
  • slope stability
  • deformation monitoring
  • SLAM (Simultaneous Localization and Mapping)
  • point cloud processing
  • mine surveying
  • ecological monitoring

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

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19 pages, 5144 KB  
Article
Study of a Fusion Method Combining InSAR and UAV Photo-Grammetry for Monitoring Surface Subsidence Induced by Coal Mining
by Shikai An, Liang Yuan and Qimeng Liu
Remote Sens. 2026, 18(5), 701; https://doi.org/10.3390/rs18050701 - 26 Feb 2026
Viewed by 363
Abstract
This study proposes a feature-level fusion method that integrates Differential Interferometric Synthetic Aperture Radar (D-InSAR) and Unmanned Aerial Vehicle photogrammetry (UAV-P) for monitoring mining-induced subsidence basin (MSB). The method begins by extracting key subsidence characteristics based on the patterns of coal-mining-related surface displacement; [...] Read more.
This study proposes a feature-level fusion method that integrates Differential Interferometric Synthetic Aperture Radar (D-InSAR) and Unmanned Aerial Vehicle photogrammetry (UAV-P) for monitoring mining-induced subsidence basin (MSB). The method begins by extracting key subsidence characteristics based on the patterns of coal-mining-related surface displacement; the centimeter-level subsidence boundary is determined from D-InSAR data, while the meter-scale deformation at the subsidence center is derived from UAV-P. These extracted features are then used to invert the parameters of the probability integral method (PIM). The subsidence basin predicted by the inverted parameters serves as a criterion to select the superior dataset between the D-InSAR and UAV-derived results. Finally, the selected subsidence data are fused to generate a composite subsidence map. The proposed method was applied to the 2S201 panel in the Wangjiata Coal Mine using eight Sentinel-1A images and two UAV surveys. The fusion results were evaluated for their regional and overall accuracy against 30 ground control points measured by total station and GPS. The results demonstrate that the fusion method not only accurately extracts large-scale deformations in the mining area, with a maximum subsidence of 2.5 m and a root mean square error (RMSE) of 0.277 m in the subsidence center area, but also precisely identifies the subsidence boundary region with an accuracy of 0.039 m. The fused subsidence basin exhibits an overall accuracy of 0.182 m, which represents a significant improvement of 83.6% and 27.8% over the results obtained using D-InSAR and UAV alone, respectively. This method effectively reconstructs the complete morphology of the mining-induced subsidence basin, confirming its feasibility for practical applications. Full article
(This article belongs to the Special Issue Applications of Photogrammetry and Lidar Techniques in Mining Areas)
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19 pages, 3356 KB  
Article
Automatic Ghost Noise Labeling for 4D mmWave Radar Data in Underground Mine Environments Using LiDAR as Reference
by Hu Liu, Zhenghua Zhang, Guoliang Chen, Jörg Benndorf and Jing Yang
Remote Sens. 2025, 17(22), 3732; https://doi.org/10.3390/rs17223732 - 17 Nov 2025
Viewed by 1312
Abstract
In underground mining environments, 4D mmWave radar performance is severely constrained by ghost noise issues resulting from multipath reflections, metal structure interference, and complex terrain, creating significant challenges for target detection, mapping, and autonomous navigation tasks. Existing research lacks efficient automated methods and [...] Read more.
In underground mining environments, 4D mmWave radar performance is severely constrained by ghost noise issues resulting from multipath reflections, metal structure interference, and complex terrain, creating significant challenges for target detection, mapping, and autonomous navigation tasks. Existing research lacks efficient automated methods and technical workflows for ghost point labeling in these scenarios. This paper presents a LiDAR-assisted two-stage ghost noise automatic labeling method. The technical workflow first achieves precise mapping between radar and LiDAR point clouds through multi-sensor spatiotemporal alignment (time synchronization and spatial registration) and then labels ghost points using a two-stage strategy that combines distance threshold filtering with density-based clustering analysis (DBSCAN). Experiments covering three typical underground mining scenarios (straight tunnels, straight tunnels with side tunnels, and cross-tunnel turns) demonstrate that the proposed method significantly outperforms single distance threshold or clustering methods in terms of precision (95.15%, 98.81%, and 98.85%, respectively), recall (97.44%, 94.68%, and 98.03%, respectively, slightly lower than distance threshold methods in straight tunnels and cross-tunnel turns), and F1 Score (95.48%, 96.70%, and 98.01%, respectively). The method exhibits efficient ghost noise detection capability and robustness in underground mining environments, providing a practical solution for optimizing radar data quality in complex confined scenarios, with potential for application in similar industrial settings. Full article
(This article belongs to the Special Issue Applications of Photogrammetry and Lidar Techniques in Mining Areas)
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Review

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35 pages, 19590 KB  
Review
Research Status, Challenges and Future Perspectives of Geological Hazard Monitoring Methods in Mining Areas
by Yanjun Zhang, Yue Sun, Yueguan Yan, Shengliang Wang and Lina Ge
Remote Sens. 2026, 18(9), 1333; https://doi.org/10.3390/rs18091333 - 27 Apr 2026
Abstract
Geological hazards induced by large-scale and high-intensity mining activities worldwide are primary drivers of regional ecological degradation and pose significant threats to human safety and property. To construct efficient monitoring systems and enhance early warning capabilities, it is essential to clarify the formation [...] Read more.
Geological hazards induced by large-scale and high-intensity mining activities worldwide are primary drivers of regional ecological degradation and pose significant threats to human safety and property. To construct efficient monitoring systems and enhance early warning capabilities, it is essential to clarify the formation mechanisms of various hazards and the suitability of corresponding technologies. Focusing on four typical geological hazards prevalent in mining areas (surface subsidence, ground fissures, landslides, collapses, and sinkholes), this paper characterizes their specific features and monitoring requirements. It systematically analyzes the physical principles, accuracy levels, and technical advantages and limitations of ground-based, aerial, and spaceborne monitoring, as well as multi-source remote sensing data fusion and emerging technologies (e.g., distributed optical fiber, light detection and range, microseismical monitoring, and deep learning). Utilizing case studies from an open-pit coal mine in Turkey and a loess gully mining area in China, the paper evaluates the effectiveness of methods like multi-temporal InSAR and UAV photogrammetry in identifying the evolution of these hazards. The findings indicate that the technological framework for mining area monitoring is transitioning from single-method approaches to integrated systems. However, given the complex mining environment, several bottleneck challenges remain, including single data dimensions, the limited environmental adaptability of aerospace remote sensing, insufficient stability of deep monitoring equipment, and weak anti-interference capabilities under extreme operating conditions. Consequently, this paper proposes that future innovations in geological hazard monitoring in mining areas will focus on multi-platform hierarchical collaboration, the development of multi-parameter fusion early warning criteria, and the construction of digital and visual platforms. Constructing a comprehensive monitoring system characterized by multi-scale collaboration and dynamic prediction capabilities is vital for improving safety standards in mining areas and achieving coordinated development between resource exploitation and environmental protection. The findings provide a theoretical foundation for the precise prevention and control of mining hazards, as well as for land ecological restoration. Full article
(This article belongs to the Special Issue Applications of Photogrammetry and Lidar Techniques in Mining Areas)
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