remotesensing-logo

Journal Browser

Journal Browser

Remote Sensing Solutions for Mapping Mining Environments

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

Deadline for manuscript submissions: closed (1 January 2023) | Viewed by 56724

Special Issue Editors

FBK-Bruno Kestler Foundation, 3DOM, Trento, Italy
Interests: photogrammetry; laser scanning; topography; mobile mapping; CH digitalization; 3D; AI
Special Issues, Collections and Topics in MDPI journals
WorldSensing, C/ Viriat 47, 08014 Barcelona, Spain
Interests: IoT; mining; monitoring; sensors; photonics; space technololgies; InSAR
Special Issues, Collections and Topics in MDPI journals
SpacEarth Technology Srl, Via di Vigna Murata 605, 00143 Rome, Italy
Interests: InSAR; optical and thermal remote sensing; photonic devices; seismic monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Raw Material (RM) and mining industrial sectors rely on various systems of infrastructures for efficient and productive operations such as plants, buildings, gas and water pipes, sewages, tailing dams, underground tunnels, transportations, etc. Such systems, normally located in harsh environments, need periodic inspection, maintenance and monitoring to maximize efficiency and minimize costs and risks. Mining infrastructures and installations are rarely renewed due to high costs involved or to ensure production continuity.

In the last years, the RM industrial sector is slowly adopting innovative techniques to improve productivity from existing assets and infrastructure, leveraging on continuous innovations in remote sensing methods, robotics, data processing methods and Artificial Intelligence. However, this digitalization process is not yet successfully and fully deployed in the mining field. More effective remote sensing solutions can be envisaged in order to innovate the RM sector and improve process efficiency.

This Special Issue, which stems from the EIT-RM project AMICOS – Autonomous Monitoring and Control System for Mining Plants (https://amicos.fbk.eu/), welcomes but is not limited to contributions in the following topics:

  • Remote sensing in mining areas;
  • Tailing dams and open pit mines monitoring;
  • Underground 3D mapping;
  • Simultaneous localization and mapping (SLAM);
  • UAV/UGV and robotics data in the mining field;
  • Data fusion;
  • Multi-temporal data processing;
  • Decision Support Systems;
  • BIM in the mining sector;
  • Spatial data analysis, modeling and visualisation;
  • Spatial analyses;
  • Case studies in mining.

You may choose our Joint Special Issue in Sensors.

Prof. Dr. Radosław Zimroz
Dr. Fabio Remondino
Dr. Denis Guilhot
Dr. Vittorio Cannas
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

  • raw material
  • mining
  • inspection and monitoring
  • unmanned air vehicles (UAV)
  • unmanned ground vehicles (UGV)
  • robotics
  • 3D mapping and visualization
  • 3D modeling
  • photogrammetry
  • remote sensing
  • LiDAR
  • SLAM
  • IoT
  • artificial intelligence
  • data fusion
  • photonics

Published Papers (15 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Review, Other

17 pages, 19440 KiB  
Article
LiDAR Point Clouds Usage for Mapping the Vegetation Cover of the “Fryderyk” Mine Repository
Remote Sens. 2023, 15(1), 201; https://doi.org/10.3390/rs15010201 - 30 Dec 2022
Cited by 2 | Viewed by 1616
Abstract
The paper investigates the usage of LiDAR (light detection and ranging) data for the automation of mapping vegetation with respect to the evaluation of the ecological succession process. The study was performed for the repository of the “Fryderyk” mine (southern Poland). The post-flotation [...] Read more.
The paper investigates the usage of LiDAR (light detection and ranging) data for the automation of mapping vegetation with respect to the evaluation of the ecological succession process. The study was performed for the repository of the “Fryderyk” mine (southern Poland). The post-flotation area analyzed is a unique refuge habitat—Natura2000, PLH240008—where a forest succession has occurred for several dozen years. Airborne laser scanning (ALS) point clouds were used for deriving detailed information about the morphometry of the spoil heap and about the secondary forest succession process—mainly vegetation parameters i.e., height and canopy cover. The area of the spoil heap is irregular with a flat top and steep slopes above 20°. Analyses of ALS point clouds (2011 and 2019), confirmed progression in the forest succession process, and land cover changes especially in wooded or bushed areas. Precise vegetation parameters (3D LiDAR metrics) were calculated and provided the following parameters: mean value of vegetation height as 6.84 m (2011) and 8.41 m (2019), and canopy cover as 30.0% (2011) and 42.0% (2019). Changes in vegetation volume (3D area) were shown: 2011—310,558 m3, 2019—325,266 m3, vegetation removal—85,136 m3, increasing ecological succession—99,880 m3. Full article
(This article belongs to the Special Issue Remote Sensing Solutions for Mapping Mining Environments)
Show Figures

Figure 1

19 pages, 9193 KiB  
Article
A Robust LiDAR SLAM Method for Underground Coal Mine Robot with Degenerated Scene Compensation
Remote Sens. 2023, 15(1), 186; https://doi.org/10.3390/rs15010186 - 29 Dec 2022
Cited by 5 | Viewed by 2897
Abstract
Simultaneous localization and mapping (SLAM) is the key technology for the automation of intelligent mining equipment and the digitization of the mining environment. However, the shotcrete surface and symmetrical roadway in underground coal mines make light detection and ranging (LiDAR) SLAM prone to [...] Read more.
Simultaneous localization and mapping (SLAM) is the key technology for the automation of intelligent mining equipment and the digitization of the mining environment. However, the shotcrete surface and symmetrical roadway in underground coal mines make light detection and ranging (LiDAR) SLAM prone to degeneration, which leads to the failure of mobile robot localization and mapping. To address these issues, this paper proposes a robust LiDAR SLAM method which detects and compensates for the degenerated scenes by integrating LiDAR and inertial measurement unit (IMU) data. First, the disturbance model is used to detect the direction and degree of degeneration caused by insufficient line and plane feature constraints for obtaining the factor and vector of degeneration. Second, the degenerated state is divided into rotation and translation. The pose obtained by IMU pre-integration is projected to plane features and then used for local map matching to achieve two-step degenerated compensation. Finally, a globally consistent LiDAR SLAM is implemented based on sliding window factor graph optimization. The extensive experimental results show that the proposed method achieves better robustness than LeGO-LOAM and LIO-SAM. The absolute position root mean square error (RMSE) is only 0.161 m, which provides an important reference for underground autonomous localization and navigation in intelligent mining and safety inspection. Full article
(This article belongs to the Special Issue Remote Sensing Solutions for Mapping Mining Environments)
Show Figures

Figure 1

18 pages, 2076 KiB  
Article
A Multi-Scale Feasibility Study into Acid Mine Drainage (AMD) Monitoring Using Same-Day Observations
Remote Sens. 2023, 15(1), 76; https://doi.org/10.3390/rs15010076 - 23 Dec 2022
Cited by 3 | Viewed by 2474
Abstract
Globally, many mines emit acid mine drainage (AMD) during and after their operational life cycle. AMD can affect large and often inaccessible areas. This leads to expensive monitoring via conventional ground-based sampling. Recent advances in remote sensing which are both non-intrusive and less [...] Read more.
Globally, many mines emit acid mine drainage (AMD) during and after their operational life cycle. AMD can affect large and often inaccessible areas. This leads to expensive monitoring via conventional ground-based sampling. Recent advances in remote sensing which are both non-intrusive and less time-consuming hold the potential to unlock a new paradigm of automated AMD analysis. Herein, we test the feasibility of remote sensing as a standalone tool to map AMD at various spatial resolutions and altitudes in water-impacted mining environments. This was achieved through the same-day collection of satellite-based simulated Sentinel-2 (S2) and PlanetScope (PS2.SD) imagery and drone-based UAV Nano-Hyperspec (UAV) imagery, in tandem with ground-based visible and short-wave infrared analysis. The study site was a historic tin and copper mine in Cornwall, UK. The ground-based data collection took place on the 30 July 2020. Ferric (Fe(III) iron) band ratio (665/560 nm wavelength) was used as an AMD proxy to map AMD pixel distribution. The relationship between remote-sensed Fe(III) iron reflectance values and ground-based Fe(III) iron reflectance values deteriorated as sensor spatial resolution decreased from high-resolution UAV imagery (<50 mm2 per pixel; r2 = 0.78) to medium-resolution PlanetScope Dove-R (3 m2 per pixel; r2 = 0.51) and low-resolution simulated Sentinel-2 (10 m2 per pixel; r2 = 0.23). A fractioned water pixel (FWP) analysis was used to identify mixed pixels between land and the nearby waterbody, which lowered spectral reflectance. Increases in total mixed pixels were observed as the spatial resolution of sensors decreased (UAV: 2.4%, PS: 3.7%, S2: 8.5%). This study demonstrates that remote sensing is a non-intrusive AMD surveying tool with varying degrees of effectiveness relative to sensor spatial resolution. This was achieved by identifying and successfully mapping a cross-sensor Fe(III) iron band ratio whilst recognizing water bodies as reflectance inhibitors for passive sensors. Full article
(This article belongs to the Special Issue Remote Sensing Solutions for Mapping Mining Environments)
Show Figures

Graphical abstract

20 pages, 5519 KiB  
Article
Activity of Okgye Limestone Mine in South Korea Observed by InSAR Coherence and PSInSAR Techniques
Remote Sens. 2022, 14(24), 6261; https://doi.org/10.3390/rs14246261 - 10 Dec 2022
Cited by 3 | Viewed by 1488
Abstract
The Okgye limestone mine, which is the largest open-pit limestone mine located in a mountainous area in Korea, suffered a collapse in 2012 that claimed four casualties. Restoration work on the rocky mined-out slopes, as well as mining and dumping activities, are still [...] Read more.
The Okgye limestone mine, which is the largest open-pit limestone mine located in a mountainous area in Korea, suffered a collapse in 2012 that claimed four casualties. Restoration work on the rocky mined-out slopes, as well as mining and dumping activities, are still in progress. Monitoring slope stability is important to prevent the sudden collapse of slopes, which can be efficiently performed by satellite-based interferometric synthetic aperture radar (InSAR) techniques. Firstly, we obtained elevation changes using InSAR-generated Copernicus 30 m DEM in 2014 and an SRTM 1Sec DEM in 2000, through which the area was roughly classified into the mining area, tailings storage area, and the mined-out area. A time series of 12-day coherence images produced by Sentinel-1B SAR were averaged annually to produce an RGB-composite image to observe the change in mining activities during 2018, 2019, and 2020. We found many persistent scatterers (PS) when observing the ground displacement, both in the ascending and descending orbits, from which we decomposed this into the vertical and east components. The largest displacement of 63.6 mm/year was observed during 2019 and 2020 in the tailings storage area in the direction of the dumping slope. For the rocky outcrops and the transmission tower, we found a seasonal oscillation, which can be interpreted as the thermal expansion of limestone and iron. This paper demonstrated that the surface stability and deformation of open-pit mines could be effectively monitored by combining InSAR DEM, coherence, and PSInSAR techniques. Full article
(This article belongs to the Special Issue Remote Sensing Solutions for Mapping Mining Environments)
Show Figures

Graphical abstract

20 pages, 14937 KiB  
Article
Rapid Photogrammetry with a 360-Degree Camera for Tunnel Mapping
Remote Sens. 2022, 14(21), 5494; https://doi.org/10.3390/rs14215494 - 31 Oct 2022
Cited by 7 | Viewed by 3671
Abstract
Structure-from-Motion Multi-View Stereo (SfM-MVS) photogrammetry is a viable method to digitize underground spaces for inspection, documentation, or remote mapping. However, the conventional image acquisition process can be laborious and time-consuming. Previous studies confirmed that the acquisition time can be reduced when using a [...] Read more.
Structure-from-Motion Multi-View Stereo (SfM-MVS) photogrammetry is a viable method to digitize underground spaces for inspection, documentation, or remote mapping. However, the conventional image acquisition process can be laborious and time-consuming. Previous studies confirmed that the acquisition time can be reduced when using a 360-degree camera to capture the images. This paper demonstrates a method for rapid photogrammetric reconstruction of tunnels using a 360-degree camera. The method is demonstrated in a field test executed in a tunnel section of the Underground Research Laboratory of Aalto University in Espoo, Finland. A 10 m-long tunnel section with exposed rock was photographed using the 360-degree camera from 27 locations and a 3D model was reconstructed using SfM-MVS photogrammetry. The resulting model was then compared with a reference laser scan and a more conventional digital single-lens reflex (DSLR) camera-based model. Image acquisition with a 360-degree camera was 3× faster than with a conventional DSLR camera and the workflow was easier and less prone to errors. The 360-degree camera-based model achieved a 0.0046 m distance accuracy error compared to the reference laser scan. In addition, the orientation of discontinuities was measured remotely from the 3D model and the digitally obtained values matched the manual compass measurements of the sub-vertical fracture sets, with an average error of 2–5°. Full article
(This article belongs to the Special Issue Remote Sensing Solutions for Mapping Mining Environments)
Show Figures

Figure 1

19 pages, 5176 KiB  
Article
Assessment of Ecological Cumulative Effect due to Mining Disturbance Using Google Earth Engine
Remote Sens. 2022, 14(17), 4381; https://doi.org/10.3390/rs14174381 - 03 Sep 2022
Cited by 5 | Viewed by 1732
Abstract
Open-pit mining and reclamation damage the land, resulting in unknown and significant changes to the regional ecology and ecosystem services. Surface mining restoration procedures necessitate a significant amount of money, typically at an unclear cost. Due to temporal and regional variability, few studies [...] Read more.
Open-pit mining and reclamation damage the land, resulting in unknown and significant changes to the regional ecology and ecosystem services. Surface mining restoration procedures necessitate a significant amount of money, typically at an unclear cost. Due to temporal and regional variability, few studies have focused on the cumulative impacts of mining activities. To investigate the ecological cumulative effects (ECE) of past mining and reclamation activities, this study continuously tracked land cover changes spatially and temporally based on phenological indices and focuses on the spatial and temporal evolution of past mining and reclamation areas using the LandTrendr algorithm. The cumulative trends of ecosystem services in the Pingshuo mining area from 1986 to 2021 were revealed using a uniform standard value equivalent coefficient. Meanwhile, the cumulative ecological effects due to essential ecosystem service functions were analyzed, including soil formation and protection, water containment, biodiversity maintenance, climate regulation, and food production. The synergistic effects and trade-offs among the functions were also explored using Spearman’s correlation coefficient. The results showed that (1) open-pit mining resulted in 93.51 km2 of natural land, 39.60 km2 of disturbed land, and 44.58 km2 of reclaimed land in the Pingshuo mine; (2) open-pit mining in the mine mainly resulted in the loss of 122.18 km2 (80.91%) of native grassland, but, through reclamation into grassland (31.30 km2), cropland (72.95 km2), and forest land (10.62 km2), the damaged area caused by mining only slightly increased; (3) the cumulative ecological value of the mining area declined by 128.78 million RMB; however, the real cumulative value per unit area was lower in the disturbance area (1483.47 million RMB) and the reclamation area (1297.00 million RMB) than in the natural area (2120.98 million RMB); (4) the cumulative value of the food production function in the study area increased, although the values of all individual functions in the study area decreased. Most of the cumulative values of services had a strong synergistic relationship. However, in the natural area, food production (FP) showed a trade-off relationship with the cumulative value of biodiversity maintenance (BM), soil formation and protection (SP), and water conservation (WC) service functions, respectively. This study constructed a methodology for analyzing mining-impacted ecosystem services using time-series processes, reproducing historically complete information for policymakers and environmental regulators. Full article
(This article belongs to the Special Issue Remote Sensing Solutions for Mapping Mining Environments)
Show Figures

Graphical abstract

23 pages, 7733 KiB  
Article
Classification of Heterogeneous Mining Areas Based on ResCapsNet and Gaofen-5 Imagery
Remote Sens. 2022, 14(13), 3216; https://doi.org/10.3390/rs14133216 - 04 Jul 2022
Cited by 11 | Viewed by 2104
Abstract
Land cover classification (LCC) of heterogeneous mining areas is important for understanding the influence of mining activities on regional geo-environments. Hyperspectral remote sensing images (HSI) provide spectral information and influence LCC. Convolutional neural networks (CNNs) improve the performance of hyperspectral image classification with [...] Read more.
Land cover classification (LCC) of heterogeneous mining areas is important for understanding the influence of mining activities on regional geo-environments. Hyperspectral remote sensing images (HSI) provide spectral information and influence LCC. Convolutional neural networks (CNNs) improve the performance of hyperspectral image classification with their powerful feature learning ability. However, if pixel-wise spectra are used as inputs to CNNs, they are ineffective in solving spatial relationships. To address the issue of insufficient spatial information in CNNs, capsule networks adopt a vector to represent position transformation information. Herein, we combine a clustering-based band selection method and residual and capsule networks to create a deep model named ResCapsNet. We tested the robustness of ResCapsNet using Gaofen-5 Imagery. The images covered two heterogeneous study areas in Wuhan City and Xinjiang Province, with spatially weakly dependent and spatially basically independent datasets, respectively. Compared with other methods, the model achieved the best performances, with averaged overall accuracies of 98.45 and 82.80% for Wuhan study area, and 92.82 and 70.88% for Xinjiang study area. Four transfer learning methods were investigated for cross-training and prediction of those two areas and achieved good results. In summary, the proposed model can effectively improve the classification accuracy of HSI in heterogeneous environments. Full article
(This article belongs to the Special Issue Remote Sensing Solutions for Mapping Mining Environments)
Show Figures

Graphical abstract

25 pages, 18892 KiB  
Article
A Novel Method of Monitoring Surface Subsidence Law Based on Probability Integral Model Combined with Active and Passive Remote Sensing Data
Remote Sens. 2022, 14(2), 299; https://doi.org/10.3390/rs14020299 - 10 Jan 2022
Cited by 16 | Viewed by 2016
Abstract
For the accurate and high-precision measurement of the deformation field in mining areas using different data sources, the probability integral model was used to process deformation data obtained from an Unmanned Aerial Vehicle (UAV), Differential InSAR (DInSAR), and Small Baseline Subset InSAR (SBAS-InSAR) [...] Read more.
For the accurate and high-precision measurement of the deformation field in mining areas using different data sources, the probability integral model was used to process deformation data obtained from an Unmanned Aerial Vehicle (UAV), Differential InSAR (DInSAR), and Small Baseline Subset InSAR (SBAS-InSAR) to obtain the complete deformation field. The SBAS-InSAR, DInSAR, and UAV can be used to obtain small-scale, mesoscale, and large-scale deformations, respectively. The three types of data were all superimposed by the Kriging interpolation, and the deformation field was integrated using the probability integral model to obtain the complete high-precision deformation field with complete time series in the study area. The study area was in the WangJiata mine in Western China, where mining was carried out from 12 July 2018 to 25 October 2018, on the 2S201 working face. The first observation was made in June 2018, and steady-state observations were made in April 2019, totaling four UAV observations. During this period, the Canadian Earth Observation Satellite of Radarsat-2 (R2) was used to take 10 SAR images, the surface subsidence mapping was undertaken using DInSAR and SBAS-InSAR techniques, and the complete deformation field of the working face during the 106-day mining period was obtained by using the UAV technique. The results showed that the subsidence basin gradually expanded along the mining direction as the working face advanced. When the mining advance was greater than 1.2–1.4 times the coal seam burial depth, the supercritical conditions were reached, and the maximum subsidence stabilized at the value of 2.780 m. The subsidence rate was basically maintained at 0.25 m/d. Finally, the accuracy of the method was tested by the Global Navigation Satellite System (GNSS) data, and the medium error of the strike was 0.103 m. A new method is reached by the fusion of active and passive remote sensing data to construct efficient, complete and high precision time-series subsidence basins with high precision. Full article
(This article belongs to the Special Issue Remote Sensing Solutions for Mapping Mining Environments)
Show Figures

Figure 1

19 pages, 5131 KiB  
Article
Monitoring Mining Activities Using Sentinel-1A InSAR Coherence in Open-Pit Coal Mines
Remote Sens. 2021, 13(21), 4485; https://doi.org/10.3390/rs13214485 - 08 Nov 2021
Cited by 14 | Viewed by 3924
Abstract
Long-term continuous monitoring of the mining activities in open-pit coal mines is conducive to planning and management of the mining operations. Additionally, this faciliatates assessment on their environmental impact and supervises illegal mining behaviors. Interferometric Synthetic Aperture Radar (InSAR) technology can be effectively [...] Read more.
Long-term continuous monitoring of the mining activities in open-pit coal mines is conducive to planning and management of the mining operations. Additionally, this faciliatates assessment on their environmental impact and supervises illegal mining behaviors. Interferometric Synthetic Aperture Radar (InSAR) technology can be effectively applied in the monitoring of open-pit mines where vegetation is sparse and land cover is dominated by bare rock. The main objective of this study is to monitor the mining activities of four open-pit coal mines in the Wucaiwan mining area in China from 2018 to 2020, namely No. 1, No. 2 (containing two mining areas), and No. 3. We use the normalized differential activity index (NDAI) based on the coherence coefficient as an indicator of the mine activity due to its robustness to temporal and spatial decorrelation. After analyzing and removing the decorrelation caused by rain and snow weather, 70 NDAI images in 12-day intervals are obtained from Sentinel-1A InSAR coherence images. Then, the annually-averaged NDAI images are applied to an RGB composite technique (red for 2018, green for 2019, blue for 2020) to express the interannual variation of the mining activities. Points of interest are then selected for NDAI time series analysis. The RGB composite results indicated that No. 1 and 3 open-pit coal mines were continuously mined during the three years; whereas, the two mining areas of No. 2 were mainly active in 2018. The 12-day NDAI time-series graphs of No. 2 open-pit coal mine also indicate that the coal piles located in the coal transferring area of the first mining area were not completely removed until April 2019. It is also seen that the second mining area was decommissioned in November 2018 and became rehabilitated in July 2019. Results were validated using the Sentinel-2A images and related background information confirming the efficiency of the proposed approach for monitoring the mining activity in open-pit mines. Full article
(This article belongs to the Special Issue Remote Sensing Solutions for Mapping Mining Environments)
Show Figures

Figure 1

19 pages, 4694 KiB  
Article
Continuous Detection of Surface-Mining Footprint in Copper Mine Using Google Earth Engine
Remote Sens. 2021, 13(21), 4273; https://doi.org/10.3390/rs13214273 - 24 Oct 2021
Cited by 13 | Viewed by 3290
Abstract
Socioeconomic development is often dependent on the production of mining resources, but both opencast and underground mining harm vegetation and the eco-environment. Under the requirements of the construction for ecological civilization in China, more attention has been paid to the reclamation of mines [...] Read more.
Socioeconomic development is often dependent on the production of mining resources, but both opencast and underground mining harm vegetation and the eco-environment. Under the requirements of the construction for ecological civilization in China, more attention has been paid to the reclamation of mines and mining management. Thus, it is the basement of formulating policies related to mining management and implementing reclamation that detection of mining disturbance rapidly and accurately. This research carries on an empirical study in the Dexing copper mine, Jiangxi, China, aiming at exploring the process of distance and reclamation. Based on the dense time-series stack derived from the Landsat archive on Google Earth Engine (GEE), the disturbance of surface mining in the 1986–2020 period has been detected using the continuous change detection and classification (CCDC) algorithm. The results are that: (1) the overall accuracy of damage and recovery is 92% and 88%, respectively, and the Kappa coefficient is 85% and 84% respectively. This means that we obtained an ideal detection effect; (2) the surface-mining area was increasing from 1986–2020 in the Dexing copper mine, and the accumulation of mining damage is approximately 2865.96 ha with an annual area of 81.88 ha. We also found that the area was fluctuating with the increase. The detected natural restoration was appraised at a total of 544.95 ha in the 1988–2020 period with an average restoration of 16.03 ha. This means that it just restores less in general; (3) it has always been the case that the Dexing mine is damaged by mining and reclamation in the whole year (it is most frequently damaged month is July). All imageries in the mine are detected by the CCDC algorithm, and they are classified as four types by disturbing number in pixel scale (i.e., 0, 1, 2, more than 2 times). Based on that, we found that the only once disturbed pixels account for 64.75% of the whole disturbed pixels, which is the majority in the four classes; (4) this method provides an innovative perspective for obtaining the mining disturbed dynamic information timely and accurately and ensures that the time and number of surface mining disturbed areas are identified accurately. This method is also valuable in other applications including the detection of other similar regions. Full article
(This article belongs to the Special Issue Remote Sensing Solutions for Mapping Mining Environments)
Show Figures

Graphical abstract

26 pages, 4135 KiB  
Article
Three-Dimensional Unique-Identifier-Based Automated Georeferencing and Coregistration of Point Clouds in Underground Mines
Remote Sens. 2021, 13(16), 3145; https://doi.org/10.3390/rs13163145 - 09 Aug 2021
Cited by 11 | Viewed by 4226
Abstract
Spatially referenced and geometrically accurate laser scans are essential for mapping and monitoring applications in underground mines to ensure safe and smooth operation. However, obtaining an absolute 3D map in an underground mine environment is challenging using laser scanning due to the unavailability [...] Read more.
Spatially referenced and geometrically accurate laser scans are essential for mapping and monitoring applications in underground mines to ensure safe and smooth operation. However, obtaining an absolute 3D map in an underground mine environment is challenging using laser scanning due to the unavailability of global navigation satellite system (GNSS) signals. Consequently, applications that require georeferenced point cloud or coregistered multitemporal point clouds such as detecting changes, monitoring deformations, tracking mine logistics, measuring roadway convergence rate and evaluating construction performance become challenging. Current mapping practices largely include a manual selection of discernable reference points in laser scans for georeferencing and coregistration which is often time-consuming, arduous and error-prone. Moreover, challenges in obtaining a sensor positioning framework, the presence of structurally symmetric layouts and highly repetitive features (such as roof bolts) makes the multitemporal scans difficult to georeference and coregister. This study aims at overcoming these practical challenges through development of three-dimensional unique identifiers (3DUIDs) and a 3D registration (3DReG) workflow. Field testing of the developed approach in an underground coal mine has been found effective with an accuracy of 1.76 m in georeferencing and 0.16 m in coregistration for a scan length of 850 m. Additionally, automatic extraction of mine roadway profile has been demonstrated using 3DUID which is often a compliant and operational requirement for mitigating roadway related hazards that includes roadway convergence rate, roof/rock falls, floor heaves and vehicle clearance for collision avoidance. Potential applications of 3DUID include roadway profile extraction, guided automation, sensor calibration, reference targets for a routine survey and deformation monitoring. Full article
(This article belongs to the Special Issue Remote Sensing Solutions for Mapping Mining Environments)
Show Figures

Figure 1

20 pages, 11881 KiB  
Article
Application of the Infrared Thermography and Unmanned Ground Vehicle for Rescue Action Support in Underground Mine—The AMICOS Project
Remote Sens. 2021, 13(1), 69; https://doi.org/10.3390/rs13010069 - 27 Dec 2020
Cited by 36 | Viewed by 3811
Abstract
Extraction of raw materials, especially in extremely harsh underground mine conditions, is irrevocably associated with high risk and probability of accidents. Natural hazards, the use of heavy-duty machines, and other technologies, even if all perfectly organized, may result in an accident. In such [...] Read more.
Extraction of raw materials, especially in extremely harsh underground mine conditions, is irrevocably associated with high risk and probability of accidents. Natural hazards, the use of heavy-duty machines, and other technologies, even if all perfectly organized, may result in an accident. In such critical situations, rescue actions may require advanced technologies as autonomous mobile robot, various sensory system including gas detector, infrared thermography, image acquisition, advanced analytics, etc. In the paper, we describe several scenarios related to rescue action in underground mines with the assumption that searching for sufferers should be done considering potential hazards such as seismic, gas, high temperature, etc. Thus, possibilities of rescue team activities in such areas may be highly risky. This work reports the results of testing of a UGV robotic system in an underground mine developed in the frame of the AMICOS project. The system consists of UGV with a sensory system and image processing module that are based on an adaptation of You Only Look Once (YOLO) and Histogram of Oriented Gradients (HOG) algorithms. The experiment was very successful; human detection efficiency was very promising. Future work will be related to test the AMICOS technology in deep copper ore mines. Full article
(This article belongs to the Special Issue Remote Sensing Solutions for Mapping Mining Environments)
Show Figures

Figure 1

Review

Jump to: Research, Other

47 pages, 5246 KiB  
Review
An Overview on Visual SLAM: From Tradition to Semantic
Remote Sens. 2022, 14(13), 3010; https://doi.org/10.3390/rs14133010 - 23 Jun 2022
Cited by 53 | Viewed by 14146
Abstract
Visual SLAM (VSLAM) has been developing rapidly due to its advantages of low-cost sensors, the easy fusion of other sensors, and richer environmental information. Traditional visionbased SLAM research has made many achievements, but it may fail to achieve wished results in challenging environments. [...] Read more.
Visual SLAM (VSLAM) has been developing rapidly due to its advantages of low-cost sensors, the easy fusion of other sensors, and richer environmental information. Traditional visionbased SLAM research has made many achievements, but it may fail to achieve wished results in challenging environments. Deep learning has promoted the development of computer vision, and the combination of deep learning and SLAM has attracted more and more attention. Semantic information, as high-level environmental information, can enable robots to better understand the surrounding environment. This paper introduces the development of VSLAM technology from two aspects: traditional VSLAM and semantic VSLAM combined with deep learning. For traditional VSLAM, we summarize the advantages and disadvantages of indirect and direct methods in detail and give some classical VSLAM open-source algorithms. In addition, we focus on the development of semantic VSLAM based on deep learning. Starting with typical neural networks CNN and RNN, we summarize the improvement of neural networks for the VSLAM system in detail. Later, we focus on the help of target detection and semantic segmentation for VSLAM semantic information introduction. We believe that the development of the future intelligent era cannot be without the help of semantic technology. Introducing deep learning into the VSLAM system to provide semantic information can help robots better perceive the surrounding environment and provide people with higher-level help. Full article
(This article belongs to the Special Issue Remote Sensing Solutions for Mapping Mining Environments)
Show Figures

Figure 1

Other

Jump to: Research, Review

12 pages, 16836 KiB  
Technical Note
Application of Multispectral Remote Sensing for Mapping Flood-Affected Zones in the Brumadinho Mining District (Minas Gerais, Brasil)
Remote Sens. 2022, 14(6), 1501; https://doi.org/10.3390/rs14061501 - 20 Mar 2022
Cited by 8 | Viewed by 2604
Abstract
The collapse of the tailing “Dam B1” of the Córrego do Feijão Mine (Brumadinho, Brasil) that occurred in January 2019 is considered a large socio-environmental flood-disaster where numerous people died and the local flora and fauna were seriously affected, including agricultural areas of [...] Read more.
The collapse of the tailing “Dam B1” of the Córrego do Feijão Mine (Brumadinho, Brasil) that occurred in January 2019 is considered a large socio-environmental flood-disaster where numerous people died and the local flora and fauna were seriously affected, including agricultural areas of the Paraopeba River. This study aims to map the land area affected by the flood by using multispectral satellite images. To pursue this aim, Level-2A multispectral images from the European Space Agency’s Sentinel-2 sensor were acquired before and after the tailing dam collapse in the period 2019–2021. The pre- and post-failure event analysis allowed us to evidence drastic changes in the vegetation rate, as well as in the nature of soils and surficial waters. The spectral signatures of the minerals composing the mining products allowed us to highlight the effective area covered by the flood and to investigate the evolution of land properties after the disaster. This technique opens the possibility for quickly classifying areas involved in floods, as well as obtaining significant information potentially useful for monitoring and planning the reclamation and restoration activities in similar cases worldwide, representing an additional tool for evaluating the environmental issues related to mining operations in large areas at high temporal resolution. Full article
(This article belongs to the Special Issue Remote Sensing Solutions for Mapping Mining Environments)
Show Figures

Figure 1

17 pages, 13676 KiB  
Technical Note
Internet-of-Things-Based Geotechnical Monitoring Boosted by Satellite InSAR Data
Remote Sens. 2021, 13(14), 2757; https://doi.org/10.3390/rs13142757 - 14 Jul 2021
Cited by 10 | Viewed by 2954
Abstract
Landslides, often a side effect of mining activities, pose a significant risk to humans and infrastructures such as urban areas, power lines, and dams. Operational ground motion monitoring can help detect the spatial pattern of surface changes and their evolution over time. In [...] Read more.
Landslides, often a side effect of mining activities, pose a significant risk to humans and infrastructures such as urban areas, power lines, and dams. Operational ground motion monitoring can help detect the spatial pattern of surface changes and their evolution over time. In this technical note, a commercial, cost-effective method combining a network of geotechnical surface sensors with the InSAR data was reported for the first time to accurately monitor surface displacement. The correlation of both data sets is demonstrated in the Gediminas Castle testbed, where slope failure events were detected. Two specific events were analyzed, and possible causes proposed. The combination of techniques allows one to detect the precursors of the events and characterize the consequences of the failures in different areas in proximity to the castle walls, since the solution allows for the confirmation of long-term drifts and sudden movements in real time. The data from the in situ sensors were also used to refine the satellite data analysis. The results demonstrate that not all events pose a direct threat to the safety of the structure monitored. Full article
(This article belongs to the Special Issue Remote Sensing Solutions for Mapping Mining Environments)
Show Figures

Figure 1

Back to TopTop