Open-Pit Mine Reclamation Monitoring and Management for a Sustainable Future Using Drone Technology: A Review

Abstract

With the advancement of drone technology, the availability of different sensors has become more reliable and cost-effective for monitoring large open-pit mine project activities. Key advantages of drone technology, including low operational expenses, rapid revisit capabilities, deployment flexibility, and high precision, have established these systems as powerful instruments for monitoring open-pit mine areas. This paper aims to provide a comprehensive review of drone technology utilization in open-pit mine reclamation monitoring. Mining 4.0 has shown promise in open-pit mine monitoring for drone deployment for use in green mining practices. This review synthesizes current research on drone survey platforms, various sensor technologies, and their practical field applications within open-pit mines for mine reclamation monitoring. This review study aims to establish a robust framework for the monitoring and management of mine reclamation. This study will provide a technically reliable reference, advancing the knowledge and application of drone technology for reclamation monitoring and management.

1. Introduction

Coal extraction yields substantial economic benefits, yet it concurrently poses numerous environmental challenges post-reclamation [1,2]. Open-pit mine land reclamation is essential for sustainable mine practices and for avoiding ecological and environmental problems [3]. Continuous management and monitoring of mine land reclamation has emerged as a paramount concern for environmental preservation in large open-pit mine projects [4]. For mine land reclamation, the open-pit industry has adopted internal overburden deposition, which reduces the reclamation cost [5]. For green mining practices, regulatory agencies have employed legislation to ensure that, following coal or mineral extraction from open-pit mines, proper mine closure and reclamation are carried out as essential measures for ecological and environmental protection. Consequently, ecological remediation efforts, such as land reclamation and revegetation, are integral to the operational lifecycle of open-pit mines. Driven by escalating environmental concerns and the global imperative for sustainable development, mine site rehabilitation and ecological restoration have become a significant focus of scientific study [6]. To mitigate the substantial environmental consequences of their operations, mining enterprises implement reclamation activities that are tailored to desired post-mining land uses, such as agriculture, forestry, or landscape rehabilitation.

In recent years, drones have become available for various applications, including disaster management [7], habitat destruction assessment [8], crop monitoring [9], vegetation mapping [10], search and rescue missions [11], open-pit mine areas [12], and infrastructure management [13]. Beyond their application in military and commercial sectors, drones have emerged as a robust airborne continuous monitoring technology within large open-pit mines [14]. Rathore and Kumar [15] demonstrated the future scope of drone applications in the mining field for smart mining operations. They present significant potential in the open-pit mine application to reduce manual labor in data acquisition, surveying, mapping, safety monitoring, machinery tracking, and infrastructure monitoring [15]. Several studies have examined the utilization of drones in the mining field [16,17]. Ren et al. [12] investigated the current research trend on the utilization of drone applications in mining areas.

Drones have been widely deployed to monitor mine activity and topographical changes, which can facilitate the creation of guidelines for mine planning and safety [12,16,17,18]. They will also be useful for mine closure, reclamation planning, and implementation. Drones, also known as unmanned aerial vehicles (UAVs), provide an economical, safe, and high-resolution alternative for effectively supervising large open-pit mine operations [19]. Using close-range drone photogrammetry, it is possible to generate realistic 3D terrain models [20,21], vegetation indices [22], contour mapping [23,24,25], surface change detection [26,27], and subsidence monitoring [25,28]. Drone-driven data significantly enhances our ability to assess the success of reclamation efforts, identify areas of concern, and document progressive rehabilitation over time. Moreover, the use of drones reduces the need for on-ground surveying in hazardous or inaccessible areas, thereby improving worker safety. This technology also enables frequent and continuous data collection, which supports real-time decision-making and adaptive management strategies for mine closure planning.

The final stage of the open-pit mining operation encompasses woodland and plant restoration, water table restoration, and ecological surveillance, together with mining site rehabilitation [29,30]. At the time of mine closure, both monitoring and management of waste deposition are essential for sustainable mining. This can be achieved by the precise monitoring of reclamation operations. Drone technology can be implemented for use in soil and water contamination surveillance, environmental rehabilitation surveillance, and land subsidence surveillance [12,31].

The benefits of drone-enabled remote detection, including rapid temporal frequency and extensive spatial coverage, allow this technology to be utilized for surveillance at the mining location without disrupting overburden (OB) disposal activities [32]. However, there are still limitations in space-borne remote sensing techniques for the precise monitoring of mine reclamation operations (e.g., high cost for fine-resolution data, atmospheric interference, low spatial resolution, temporal resolution challenges, calibration for ground truth and spectral variability). The use of drone-based remote sensing platforms is a reliable, robust, and cost-effective solution for large open-pit mine projects, and can provide accurate post-reclamation operation monitoring [33,34].

Given the increasing emphasis on sustainable mining practices, regulatory compliance, and corporate social responsibility, this study aligns with national and global objectives for environmental stewardship. The focus of this study is how efficiently we would optimally and exhaustively use drone technology to improve decision-making processes for monitoring mine reclamation operations, reclamation area changes, and subsidence analysis, using integrated multidisciplinary approaches, as seen in Figure 1. A futuristic approach to monitoring open-pit mine reclamation should encompass a comprehensive assessment of area changes, subsidence, and vegetation profiling, while actively involving all relevant stakeholders to ensure effective hazard prevention and sustainable land restoration.

2. Research Status of Drone Applications in Open-Pit Mine Areas Based on Literature Review

Multiple documents (encompassing patents, articles and symposium proceedings) concerning drone system implementation in open-pit mining regions have been published between 2010 and 2025. Relevant documentation was collected from internationally published information sources. All research examined was published before January 2025. The investigations were predominantly from the United States of America (238 publications), China (202 publications), Brazil (74 publications), Germany (57 publications), India (47 publications), Ice-land (47 publications), France (42 publications), Australia (38 publications), and Canada (29 publications), as well as various other nations (Figure 2).

A total of 1156 works have been published in the Web of Science (WoS) core collection database between 2005 and June 2025 on the following topics: “Open-Pit Mine Surveillance Using UAV Technology” and search terms are “Open-pit mines”, “UAV survey”, “UAV close range photogrammetry”, “Mine reclamtion monitoring”, “3D change detection” and “Ground subsidence monitoring”. The investigations were predominantly from China (411 publications), Iran (108 publications), Canada (101 publications), the USA (86 publications), Australia (79 publications), and Russia (48 publications), as well as various other countries, as seen in Figure 3.

Keyword term occurrence in the released publications was examined using the program VOSviewer version 1.6.20; results are shown in Figure 4. The highest occurrence rates per term were as follows:“open-pit mine” (89 times), “slope stability” (55 times), “3D change detection” (27 times), “remote sensing” (27 times), “drone survey” (23 times), “UAV” (17 times), “GIS” (12 times), and “photogrammetry” (12 times). The advent of UAV close-range photogrammetry has substituted conventional approachesand has become a reliable, robust, and cost-effective technology for thorough investigations in open-pit mines. Bibliometric evaluation has revealed that the utilization of drones in open-pit mines remains in its early stages; present research still concentrates primarily on surveying terrain for slope instability, risk surveillance, and environmental evaluation.

2.1. Publication and Citation Analysis

A title and keywords-based search strategy was used to acquire peer-reviewed published literature specifically addressing the investigation of open-pit mine monitoring using drone technology. The dataset utilized was sourced from the WoS core collection. The appropriateness of these publications was further confirmed using certain exclusive/inclusive criteria in either the topic or keywords. Topic filtering was used to eliminate articles that did not pertain to studies on open-pit mine monitoring using drone technology. We developed our search terms to encompass the following topics: “Open-pit mines”, “Drone survey”, “UAV close range photogrammetry”, “Mine reclamation monitoring”, “3D change detection”, and “Ground subsidence monitoring”.

Throughout the timeframe of the examined research, there was a significant increase in the volume of scholarly articles, with the number of publications growing from 4 in 2005 to 174 in 2024. In 2024, there were 174 articles published, making it the year with the highest number of article publications. Figure 5 presents the increasing trend of publications and citations from 2005 to June 2025 on monitoring open-pit mine activities using drone technology.

2.2. Research Areas and Source Journals Analysis

Within the WoS database, the collected and analyzed publications are classified into numerous separate research domains. Nevertheless, because of the restricted quantity of publications in various disciplines, only the findings from the leading 15 research domains are examined, as illustrated in Figure 6. Among the articles related to open-pit mine monitoring using drone technology, the top five research areas in terms of article count are as follows: Environmental Sciences (269), Geosciences Multidisciplinary (260), Mining Mineral Processing (214), Remote Sensing (120), and Engineering Geological (86). Figure 7 shows the proportional percentage of articles across these 10 research fields. The top three disciplines have publication rates exceeding 10%.

3. Application of Drones and Sensors

This review shows that drones have been widely deployed in field applications, including fixed-wing and multi-rotor models, with digital cameras being the predominant sensors employed. The integration of various sensors—including digital cameras, spectral imaging sensors, LiDAR, thermal infrared cameras, gas sensors, ultrasonic sensors, laser range finders, and ultra-wideband radar—is crucial for obtaining diverse sources of surveillance information in order to promote the utilization of drones in open-pit mining regions.

The development of various technologies has enabled the successful transformation of drones from defense to commercial applications [35]. A wide variety of drones currently meet the specific needs for scientific investigation along with various additional applications, including farming, wildfire surveillance, waterway surveillance, ecological surveillance, meteorological tracking, monitoring, structural examinations, cargo transport, and recreation [36]. Although there are numerous aerial vehicle configurations available, encompassing fixed-wing drones, multi-rotor drones, and autonomous aircraft, fixed-wing and multi-rotor drones represent a substantial majority of those utilized in scientific investigation [12,37]. Fixed-wing drones resemble commercial aircraft, characterized by a rigid wing configuration. Fixed-wing drones demonstrate prolonged endurance and enhanced flight velocities, enabling a varied range of surveillance operations. The fixed-wing drone’s inability to launch and descend vertically requires a particular launch distance, creating difficulties for operations in restricted spaces. The fixed-wing drone has a constrained payload capability and is influenced by wind velocity during launch and descent. Fixed-wing drones, including the Sensefly eBee and Skywalker, are widely employed in open-pit mines (Figure 8).

Multi-rotor wing drones resemble helicopters, using wing rotation to generate lift. Due to their variable flying speeds and operational flexibility, they are extensively utilized in confined spaces, requiring only low-altitude flights. The multirotor wing drone is capable of vertical takeoff and landing, in addition to hovering mid-air. This drone can capture photos from various angles to obtain diverse perspectives of an object. Nevertheless, it is essential to note that rotary-wing drone platforms are less appropriate for prolonged missions due to their restricted battery life. In contrast to fixed-wing drones, the rotary-wing platform of a multi-rotor drone offers substantial benefits in constrained or restricted areas. The DJI Phantom series (comprising Phantoms 2, 3, and 4), DJI Air 2S, DJI Matric 300 RTK, DJI Mavic 3, and the AscTec Falcon 8 are the most prevalent in surface mines. Rotary-wing drone technology provides a dynamic, continuous, and cost-effective approach to data acquisition, offering significant advantages in monitoring in open-pit mine areas over traditional measurement techniques or remote sensing (

Table 1. Assessment of drones and alternative surveillance techniques in open-pit mines [12].

Monitoring Methods	Monitoring Information	Data Process	Price	Period	Working Conditions
GPS	Point/line	Fast	High	Shorter	All weather conditions
Satellite remote sensing	Point/line/space	Fast	High/lower	Shorter	Depends on weather
InSAR	Point/line/space	Fast	Lower	Shorter	All weather conditions
Drones	Point/line/space	Fast	Lower	Shorter	Depends on weather
GBSAR	Point/line/space	Fast	High	Real time	All weather conditions
Tachometry	Point	Fast	Lower	From seconds (robotic) to periodic	Depends on weather
TLS	Point/line/space	Slow	Moderate	Hours to days	Depends on weather

).

Drones offer an aerial platform; the effectiveness of the monitoring mission relies on the detection instruments that the drones carry. Detection instruments are categorized into two groups based on their operation: active detectors, and passive detectors. Passive detectors utilize electromagnetic radiation detection, including digital photography equipment or hyperspectral imaging equipment; this mode of detection exclusively gathers illumination radiation that is directly bounced from solar sources or from terrestrial objects. Active detectors transmit electromagnetic radiation toward terrestrial objects, subsequently gathering data bounced backwards from them; these include LiDAR, SAR, and additional systems [12]. Due to the payload and durability of drones, their sensors tend to be lighter and smaller. Representative sensors are widely used in various field applications for scientific research.

Various sensor types have been effectively employed in precision agriculture [9,38], ecological evaluation [39], surveying and mapping [40], slope monitoring, and management [40,41,42], as well as in other domains. Nonetheless, their implementation in open-pit mining regions requires further exploration in order to promote sustainable mining practices.

Table 2. Sensors used for drone survey in various field applications [12].

Sensor Type	Pixel Resolution	Purpose
Digital Camera (Sony A5000, SonyQX100 and Canon IXUS 125HS)	54,569 × 3632 54,729 × 3648 46,089 × 3456	Drones with digital cameras are utilized in order to obtain high-resolution photographs of mining sites. They capture color images in the RGB spectrum from visible light (400–760 nm). This method is cost-effective and provides high-resolution imagery. With progress in computer vision, techniques like Structure-from-Motion (SfM) algorithms can reconstruct terrain [43].
Spectral Imaging Camera (Senop Rikola, MicaSense RedEdge and Parrot Sequoia)	10,109 × 1010 12,809 × 960 12,809 × 960	Commonly utilized multispectral sensors, including the Parrot Sequoia (4 channels: Red, Green, Near-Infrared, RedEdge) and MicaSense RedEdge (5 channels: Red, Green, Blue, Near-Infrared, RedEdge), employ individual lenses and filters for various wavelengths. These prove to be efficient in precision farming and vegetation analysis, and demonstrate significant potential in crop disease identification and land-use classification [44,45].
LiDAR (Zenmuse L1 and L2)		Able to obtain high-density 3D point cloud information in real time. This provides advantages over conventional ground-based surveying and photogrammetry, especially in isolated or mountainous landscapes where ground control points are restricted [46].
Thermal Infrared Camera (FLIR Tau2 324 and ICI thermal camera)	3249 × 25	Thermal imaging is useful for earth surface observation, heat assessment, and monitoring crop stress, lodging, and disease. In geological surveys, it can provide temperature mapping over large areas [47] (e.g., km2), making it suitable for challenging environments such as volcanoes and geothermal zones, and enabling accurate heat flow analysis [47]. This sensor will be useful for thermal mapping of open-pit coal mine slopes due to their spontaneous heating.
Gas Sensor		This is used to monitor air quality, particularly in areas affected by mine blasting, haze, and photochemical smog. Mainly used for dust. Sensors can detect and simulate the behaviour of pollutants such as PM10, CH4, and CO2 to study their distribution, diffusion, and transmission characteristics, supporting environmental protection efforts [48,49,50,51].
Ultrasonic Sensor		This sensor is useful for obstacle detection in the mine by radiating high-frequency sound waves and collecting reflected waves.
Laser Range Finders		This is an expensive sensor that is generally used for obstacle detection.
Ultra-Wideband Radar		This sensor has several special features that make it reliable and suitable for use in harsh environmental conditions, such as fog, smoke, dust, rain, and gas. This sensor is used for precise obstacle detection using electromagnetic waves.

shows each type of sensor along with various field applications.

4. Application of Drones in Open-Pit Mine Reclamation Monitoring

Restoring land and ecological systems in mine areas is the main objective of reclamation monitoring; this is an essential step in harmonizing human and environmental needs [52,53]. Currently, research related to mine site restoration has evolved from monitoring basic land degradation to more comprehensive tracking of land and environmental changes, covering everything from site-specific degradation metrics to the entire degradation management process. Although initiatives in land rehabilitation and ecosystem recovery have yielded many positive outcomes, ongoing issues in theoretical understanding, technical solutions, policy frameworks, and regulatory oversight suggest that some countries continue to experience lower rates of successful restoration. Surveillance after both land rehabilitation and ecological restoration in mining territory is a vital component of the procedure, since it is the foundation for enhancing both rehabilitation techniques and operational effectiveness [54,55].

Suitability evaluation after land rehabilitation in open-pit mine sites is usually based on the design parameters of the rehabilitation scheme (including topsoil removal and restoration, rehabilitation altitude, terrain levelness, topsoil density, soil humidity, and organic content) and their connection to real circumstances. The evaluation and surveillance of land and environment after rehabilitation (e.g., agricultural production, vegetation coverage) are essential elements of rehabilitation activities. Multiple investigations have demonstrated that the species composition and spatial distribution of vegetation cover can be utilized to forecast rehabilitation outcomes [56]. Further investigation suggests that post-rehabilitation assessment is mainly reliant on terrain characteristics and the precision of vegetation classification [45]. SPOT and Landsat satellite data can be used to examine the effects of reclamation by determining vegetation coverage in the study region. Nevertheless, ecological evaluations after mining rehabilitation often require precise charts of land utilization and plant coverage [57], as well as ongoing monitoring of their evolution [56]. Satellite pictures frequently fail to meet reclamation monitoring and evaluation criteria due to their low geographical and temporal resolution [58]. Drones can consistently acquire detailed photographs at minimal elevations, proving their suitability as information-gathering instruments for plant coverage and terrain utilization modifications. Drones can connect the divide between ground-based sampling and orbital remote sensing detection. These systems deliver extensive detailed data for assessment and monitoring following restoration, yielding promising outcomes in plant coverage evaluation [45,59].

Forest and vegetation recovery, groundwater replenishment, and monitoring of environmental pollutants are integral to the final stage of open-pit mine reclamation, taking place alongside its closure. In this study, the reviewed literature was organized based on the exact type of rehabilitation accomplished using drone technology: monitoring soil and water pollution, tracking environmental recovery, and observing land subsidence.

4.1. Monitoring of Soil and Water Pollution

Several publications on environmental pollution monitoring have indicated that drones are predominantly deployed to obtain hyperspectral imagery and to evaluate the concentration levels of hazardous substances. Furthermore, studies have shown that drones are commonly employed for data collection through onboard sensor systems, alongside diverse operational tasks such as aquatic specimen retrieval procedures.

A drone was deployed to chart gamma radiation from uranium deposits in Cornwall, the United Kingdom, by Martin et al. [60]. The South Terras site, a former extraction area containing these resources, served as a prominent source of uranium and radium minerals from 1870 to 1930. At present, these areas exhibit elevated radioactivity. In addition, outdated documentation for the region restricts the effectiveness of conventional survey techniques. To detect incoming radiation, a portable gamma-ray detector sensor was attached to the drone. The data was recorded on the device and relayed instantaneously to the remote-control station personnel. Thus, they were able to produce detailed radiation maps across large areas at significantly higher collection rates when using drone-based platforms. Additionally, it was conclusively shown that the shielding effect caused by a human operator carrying a detector—common in standard ground-based measurements—can be completely eliminated through the use of drone-mounted instruments.

Fang et al. [61] developed a method using drone-captured hyperspectral imagery and regression analysis to assess and measure iron concentrations present in soil. The Malan Fe mine in Hevei Province, China, was chosen as the experimental site to validate the suggested approach. They initially created a GPU-enhanced Python 3.9 script to process drone-acquired hue saturation intensity (HSI) images. Afterwards, this process was integrated into the PhotoScan platform. The resulting model underwent unbiased validation through cross-validation methods. The approach with the highest predictive performance was selected. The regression techniques tested included partial least squares (PLSR), support vector machine (SVM), and artificial neural network (ANN). The results indicated that PLSR was the most effective for iron concentration estimation. Finally, feature extraction and selection were applied to further improve estimation precision.

Jackisch et al. [62] studied pyrite and its resulting weathered compounds in mining remnants, using a drone. The investigation took place at a restored tailings area within the Sokolov lignite basin in the Czech Republic. Researchers conducted four sets of both terrestrial and aerial surveys. Field measurements—including pH, X-ray fluorescence (XRF), and reflectance—were used to verify drone performance, providing accurate reference data for laboratory-tested specimens. Hyperspectral datasets were corrected for atmospheric, surface, and lighting variations using a detailed digital elevation model (DEM). In addition, point cloud data (PCD) and DEMs were constructed from drone-captured RGB imagery using the SfM algorithm and multi-view stereo techniques. Analysis of the hyperspectral imagery confirmed the presence of jarosite and goethite at the site. Figure 9 displays the combined results of acid mine drainage identification collected over the course of the study.

Drone technology offers several advantages in the surveillance of pit lakes: (1) it mitigates the dangers linked to water sampling, (2) it decreases the expenses related to sampling, and (3) it accelerates the pace of data gathering. Consequently, Castendyk et al. [63] used drones to identify the appropriate depth for water sampling in a quarry lake. The optimal depth was established by measuring temperature and specific conductivity in the area, data that the drone collected prior to obtaining water samples. Details about the lake’s physical properties were then relayed to the researcher for further analysis. They performed an investigation on two mine lakes situated in the northwest region of the USA. Both locations are challenging for people to access. The drone was fitted with a conductivity temperature depth (CTD) instrument capable of measuring up to a depth of 100 m. A water collection device capable of extracting 2 L of water specimens from a depth of 120 m was securely installed. Three separate water samples were collected from the initial quarry lake at depths of 0, 28, and 56 m. Laboratory analysis revealed consistency across these samples. In contrast, results from the second lake indicated differences in water composition and persistent summer stratification at various depths. The experimental results indicate that employing drones for monitoring pit lakes can yield significant insights into their behaviour and water quality for management purposes.

4.2. Monitoring of Ecological Restoration

Throughout the rehabilitation stage, the utilization of drones in environmental rehabilitation surveillance became significant for green mining practices. In environmental rehabilitation surveillance, drone systems are employed to examine plant distribution by obtaining aerial photographs, multispectral photographs, and near-infrared photographs.

Photogrammetry using drones was utilized to study and track modifications in the environmental recovery zone of open-pit limestone quarries in Gangwon-do, Korea, by Lee et al. [64]. Researchers performed aerial laser scanning and collected imagery using both fixed-wing and multi-rotor drones over three separate time periods. Orthoimages were then generated, along with digital surface models (DSMs) and point clouds, and changes in the restoration zone were evaluated by comparing these products to orthoimages and elevation models. Plant coverage was mapped from the RGB orthoimage using the excess green and visible atmospheric resistance indices, revealing an increase in vegetation of about 10–30% by area. Additionally, the DEM from drone photogrammetry closely matched the planned restoration boundary, confirming that the rehabilitation process has been successfully completed.

Urban et al. [65] employed two drones in order to collect data from waste dump sites during dormant weather conditions; this data was then compared with airborne photogrammetric results. Furthermore, in order to assess the accuracy of drone-derived point clouds, an innovative approach for statistical assessment was presented that requires no supplementary verification data (e.g., GNSS measurements). Researchers examined the influence of plant coverage and chose typical vegetation locations, including forest, prairie, and brush areas, to detect systematic errors in the data collected by the drone. A review of specific data in grass regions found that the deviation in accuracy was roughly 0.03 m, with mean displacement varying from 0.01 to 0.08 m. Concurrently, data accuracy in the forest environment was 0.04 m, which was lower than in the prairie. The data from the brush habitat appeared to be more precise than the data from the grass and forest regions.

Moudrý et al. [66] utilized fixed-wing drone platforms to create a digital terrain model (DTM) to evaluate plant coverage (grasslands and woodlands) and platform capabilities. The study was conducted using the following methodologies: (1) comparative analysis was carried out between the eBee system, using an adjustable lens, and the EasyStar II, featuring a fixed lens; (2) drone photography was used to create a DTM of the vegetation coverage under dormant weather conditions; (3) assessment of potential enhancement of inferior-quality photographs was performed via increased photograph quantities; and (4) the DTM derived from drone photography was contrasted with results generated via airborne laser scanning (ALS) in the Czech Republic. EasyStar demonstrated superior point density and accuracy relative to eBee. The accuracy was enhanced following terrain filtering within the point cloud for the forest setting, yielding a root mean square error (RMSE) of 0.11 for EasyStar and 0.13 for eBee across each platform. The accuracy for the prairie setting was marginally diminished. Both systems demonstrated the capability to accurately detect topography in open prairies and beneath forest canopies under dormant weather conditions, regardless of variations in acquired point clouds; both systems achieved better accuracy than the national LiDAR-based DTM. [17]. Moudrý et al. [67] employed point datasets from airborne photogrammetry and LiDAR to detect post-mining locations.

Padró et al. [59] introduced a cost-effective and feasible method for overseeing open-pit mine restoration through drone imagery. In order to spectrally correct the drone sensor data, researchers took ground spectroradiometer readings using a drone that was fitted with a multispectral instrument. In this research, a multispectral device with four spectral ranges was used: green (530–570 nm), red (640–680 nm), red-edge (730–740 nm), and near-infrared (770–810 nm). Additionally, images were analyzed in order to generate spectral information, vegetation and soil metrics, structural characteristics, and land cover maps. The generated wavelength data and surface coverage classification assisted in detecting and quantifying mining debris distribution, exposed earth, and other surface coverage extensions. Furthermore, they facilitated the evaluation of flora spread and plant development, allowing a graphic and straightforward comparison to the surrounding baseline system.

Strohbach et al. [68] used high-resolution near-infrared aerial photos and ground-based observations to investigate the extent of vegetation restoration that had been achieved as a result of rehabilitation techniques.

4.3. Monitoring of Ground Subsidence

The monitoring of areas where ground subsidence occurs is exceedingly difficult for humans due to safety concerns. Therefore, the importance of utilizing drone technology to easily gather information in areas that are difficult to reach is growing. The drones used in this investigation were employed in mine waste facilities and ground settlement regions that resulted from underground extraction operations.

Researchers employed drone photogrammetric techniques in order to create precise and dependable subsidence inventory maps for abandoned extraction sites [25]. The research location was Samsung limestone quarry in Cheongwon-gun, Chungcheongbuk-do, Korea, where aerial photographs were captured using drones. Photographs were processed using information taken from strategically placed survey control markers (GCPs) to create georeferenced orthoimages and DTMs. Consequently, incidents of sinkhole-style subsidence along with their geographic locations were identified. Figure 10 shows the results of the subsidence area monitoring using a drone survey. In Figure 10d the A and A’ shows the distance among two points in the digital terrain model (DTM)

Rauhala et al. [69] deployed an UAV platform to assess the possible subsidence of mining waste materials in cold-region extraction sites. SfM photogrammetric techniques were employed to create yearly terrain models of the waste material surface, facilitating observation of ground deformation. Survey reference points measured within a stationary zone of the containment facility served to validate model accuracy. The results of the investigation revealed that the detected ground deformation was linked to surface degradation, waste material consolidation, and compaction of the subsurface organic substrate.

Dawei et al. [70] utilized drone photogrammetry to observe active ground settlement depression originating from a subterranean coal mine, and presented a technique for quickly acquiring variables associated with mine settlement. The researchers obtained DEMs for two distinct periods, utilizing a drone; subsequently, they created a Surface Deformation Subtraction Model (SDSB) by subtracting the two models. The mining settlement was evaluated using the dynamic inversion method.

Table 3. Use of drones in open-pit mines for reclamation monitoring.

Drone Type	Objective	Year	Data Acquired	Reference
Rotary wing	Charting gamma radiation using a drone-mounted compact gamma-ray detector	2015	Gamma spectrum data	Martin et al. [60]
Rotary wing	Tracking alterations in environmental rehabilitation using drone photogrammetry	2016	Digital camera image	Lee et al. [64]
Rotary wing	Development of a settlement catalog chart using drone photogrammetry	2017	Digital camera image	Suh and Choi [25]
Fixed wing	Surveillance potential settlement of tailings	2017	Digital camera image	Rauhala et al. [69]
Fixed wing	Evaluation of plant establishment through near-infrared airborne imagery	2018	Multispectral image	Strohbach et al. [68]
Fixed wing	Analysis of airborne survey findings using two different drones	2018	Digital camera image	Urban et al. [65]
Rotary and fixed wing	Examination of pyrite and weathering byproducts in mining waste	2018	Hyperspectral image	Jackisch et al. [62]
Rotary wing	Ideal water sampling depth through temperature and specific conductance	2019	Water temperature data, Water sample	Castendyk et al. [63]
Fixed wing	DTM creation related to vegetation cover and drone system evaluation	2019	Digital camera image	Moudrý et al. [66]
Fixed wing	Evaluation of aerial photogrammetry point clouds using LiDAR	2019	Digital camera image	Moudrý et al. [67]
Rotary wing	Monitoring mine restoration activities using UAS multispectral imaging	2019	Multispectral image	Padró et al. [59]
Unknown	Quantifying iron levels in sediment via hyperspectral analysis and regression modelling	2019	Hyperspectral image	Fang et al. [61]
Fixed wing	Monitoring dynamic subsidence caused by underground mining	2020	Digital camera image	Dawei et al. [70]
Rotary wing	Identifying areas eroded by surface runoff	2022	Digital camera image	Padró et al. [71]
Rotary wing	Open-pit mine soil erosion and land degradation monitoring	2022	Digital camera image	Xiao et al. [72]
Rotary wing	Landform (slope geometries) measurements and boundary checks were conducted using the drone-based PPK method	2025	Digital camera image	Türk et al. [73]

shows the studies that used drones in mine reclamation surveys.

5. Future Prospects of Drone Technology and Computer Vision for Mine Reclamation Monitoring

This investigation seeks to create a dependable, sturdy, and economic method for open-pit mining rehabilitation surveillance. This section focuses on how the integrated multidisciplinary approach will be implemented, specifically at the reclamation site (see Figure 11).

5.1. Data Collection

5.1.1. Image Acquisition

PCD, DEM, DSM, and DTM image acquisition of the open-pit mine reclamation area is the primary and one of the most crucial phases in reclamation monitoring. Operating a drone in a rehabilitation zone is a demanding undertaking. One functional challenge linked to 3D reconstruction using drone-captured imagery is identifying the optimal flight settings for precise 3D model reconstruction without unduly prolonging flight duration or data preprocessing time. Multiple factors, including flight elevation, meteorological conditions, and regional flight rules, are just some of the numerous considerations requiring evaluation prior to flight operations [74,75]. This research presents the drone flight planning utilized in image data acquisition. The drone operation mode is a critical aspect in flight planning, as drones adhere to predetermined routes in order to precisely cover the target area. Based on mission requirements, platform characteristics, and environmental factors, flights are typically executed in manual, assisted, or autonomous modes (see Figure 12). The flight method substantially influences image data gathering.

Based on the literature review, the S-path pattern is preferred for image data acquisition from the mine reclamation site, as seen in Figure 13. Image overlapping is a key factor upon which the model’s overall accuracy relies [77,78]. The overlap between images must be adequate in order to construct precise 3D models, generally necessitating a forward overlap exceeding 75% and a lateral overlap surpassing 60% [79].

5.1.2. Ground Control Points

Ground control points (GCPs) represent a procedure carried out alongside the imagery acquisition process. These are used for the geo-referencing of the DEM, and improve vertical elevation accuracy [81,82]. These are monitored using a Total Station or DGPS survey. GCPs are distributed at the mine reclamation site, as shown in Figure 14. Typically, a minimum of 6–7 GCPs is necessary for geo-referencing and to enhance the precision of the DEM [42,79]. Achieving high-resolution topographic DEMs requires thoughtful selection of the flight mode and appropriate placement of GCPs at the monitoring site.

5.2. Processing of Drone Imagery Data and 3D Model Reconstruction

In order to achieve precise 3D reconstruction, it is essential to capture images that are properly exposed to the target object, scene, or surface during image collection, using a drone survey.

5.2.1. Pre-Processing of Image Data

The initial processing starts by importing the collected images into the photogrammetry software. Figure 15 the shows open-source and commercial image processing tools used for 3D model reconstruction and point cloud generation, along with their processing efficiency. Advancements in image analysis and computer vision methods have led to the creation of 3D reconstruction methods using a photogrammetry technique known as SfM. When integrated with Multi-View Stereo (MVS), this method enables the fully automated generation of detailed DEMs [83]. The initial phase of data processing is feature detection, which entails locating feature points in multiple comparable images. Identifying feature points that remain consistent despite variations in scale and orientation is crucial for establishing matches over a wider area [43,84]. Geometric distortions hinder the identification of identical features irrespective of differences in scale or orientation [85]. The characteristics or critical points are filtered to retain only correct matches (see Figure 16).

5.2.2. Point Cloud Generation and 3D Reconstruction

The point cloud seeks to achieve a 3D realistic representation of the mining rehabilitation zone in various file formats, including DEM, DSM, DTM and ortho-mosaic imagery. Sparse point clouds were generated by matching points in stereo images. In sparse point clouds, point density is substantially lower relative to dense point clouds. Bundle adjustment (BA) helps minimize artifacts or irregularities that may arise from spare point clouds [86]. This technique is applied to maximize the reliability of the model by refining its parameters and improving overall structural accuracy.

Figure 15. Evaluation of photogrammetry software with respect to data processing time and reconstruction [87].

Figure 15. Evaluation of photogrammetry software with respect to data processing time and reconstruction [87].

Figure 16. Three-dimensional model reconstruction using the SfM algorithm.

Figure 16. Three-dimensional model reconstruction using the SfM algorithm.

5.2.3. K-Means Clustering and Multi-Model Testing

Three-dimensional modelling has extensive industrial applications in sectors such as the automotive industry, healthcare, and construction. However, surface scan noise, cumulative alignment errors, and flawed data integration frequently cause distortions in the surface of objects that have been reconstructed from multi-viewpoint range images; these distortions include dense patches, erroneous links, indistinct features, and artifacts. Additionally, current fusion techniques typically require significant computational resources and storage capacity. To address integration inaccuracies and enhance the precision of combined point positions, the k-means clustering method is utilized for optimization purposes [88,89,90].

5.3. Point Cloud Data Alignment

The iterative closest point (ICP) and picking point algorithm is widely used for aligning and comparing PCD against the reference for change detection, displacement, and ground subsidence monitoring [41,91,92]. It works by iteratively minimizing the distance between corresponding points in the point clouds and refining the transformation (rotation and translation) with each iteration. The algorithm starts with an initial guess of the alignment and progressively reduces the misalignment by matching points in PCDs. ICP is particularly effective for fine-tuning the alignment of point clouds that already have a rough registration [93].

5.4. Three-Dimensional Change Detection

Photogrammetry technology and computer vision methods are reliable for large- and small-scale earth surface 3D change detection and displacement monitoring [27,94]. These methods are classified into three groups. The first consists of conventional techniques (distance-based approaches), the second encompasses machine learning approaches utilizing manually designed features, and the third involves deep learning techniques that automatically extract abstract attributes without requiring user intervention.

Distance-based change detection methods are widely used for detecting subsidence, slopes, and land erosion analysis [95,96,97]. Distance-based change detection methods have proven reliable for surface change and subsidence analysis [98]. Three-Dimensional change detection methods use PCDs for calculating the distance between two PCDs. Different methods of change detection are shown in Figure 17.

C2C, C2M, and M3C2 are point-cloud-based change detection methods that can find changes in compared PCDs. Small- and large-scale changes can be monitored using the drone photogrammetry technique [42].

The distances between the two PCDs (e.g., reference PCD and compared PCD) are determined by employing the principle of nearest neighbour distance. For each point in the compared PCD, the adjacent point in the reference PCD is investigated, and the Euclidean distance between the two points is calculated (Equation (1)). The C2C technique calculates the minimal distance between matching points in two point cloud datasets. For each point $P_1(x_1,y_1,z_1)$ in the first cloud, the closest point $P_2(x_2,y_2,z_2)$ in the second cloud is determined, and the Euclidean distance is calculated as follows:

$$d\left(P_1,P_2\right)=\sqrt{{(x_2-x_1)}^2+{\left(y_2-y_1\right)}^2+{\left(z_2-z_1\right)}^2}$$

(1)

Figure 18 illustrates the principle of measuring using the C2C method. In order to achieve a more precise estimation of the actual distance to the reference surface, a local surface model has been implemented. Figure 19 presents the methodology for determining C2C distance within the model. C2C involves directly comparing two PCDs by calculating the spatial differences between corresponding points, making it effective for detecting small- and large-scale changes like terrain shifts or significant waste deposition activities. However, it can struggle with small-scale changes and is sensitive to noise and outliers.

C2M, on the other hand, compares a point cloud with a 3D mesh model, calculating the distance between points in the cloud and the mesh. This method is particularly useful when comparing raw PCD with an existing model, and is often used for infrastructure or construction monitoring. Its effectiveness depends on the quality and accuracy of the mesh.

M3C2 is a more advanced method that employs a multiscale approach, comparing a 3D model with a point cloud at multiple levels of detail. This technique is highly accurate for both large- and small-scale 3D changes, making it ideal for monitoring fine deformations or small structural changes, but it requires high-quality models and is computationally expensive. Each method has its advantages and is selected based on the scale and type of change, data quality, and computational resources available.

This approach employs a three-phase procedure utilizing two point clouds from epochs A and B (Figure 20a). Phase one involves planar surface extraction via region-growing segmentation (Figure 20b). Phase two conducts a correspondence search for planes using a binary random forest classifier (Figure 20c). Phase three performs quantification of change and uncertainty (detection level) through M3C2 distance computation between corresponding planes (Figure 20d).

Figure 20 shows the principal approach for the distance-based change detection analysis using the M3C2 algorithm. The M3C2 method can be used to examine surface change and subsidence in the reclamation area. This method is suitable for complex terrain conditions; its identification of land surface change is highly accurate at the centimeter level. Figure 21 shows the workflow for both change detection and subsidence monitoring using computer vision.

In the era of Industry 4.0, for open-pit mine reclamation operations, completely autonomous and remotely controlled systems are being developed and implemented as a result of scientific and technological advancements. Data obtained using UAVs can be used to generate mixed-reality (MR) models. MR blends the real and digital worlds to produce a smooth, natural, and intuitive 3D interaction between humans, computers, and virtual environments [99,100]. This emergent reality is enabled by advancements in computer vision, graphics processing, display technologies, input methods, and cloud computing [101]. MR uses robust sensing and imaging technology to enable users to interact with and modify real and virtual objects and settings. MR’s straightforward and user-friendly design provides an accessible solution for people of various ages and technical abilities to be able to engage with connected themes or issues [102]. MR has several potential applications, including planning, training, and management. Furthermore, UAVs and artificial intelligence (AI) technology can be utilized to collect and analyze data from mine sites for open-pit mine reclamation management. Additionally, these data can then be used to create MR models to visualize reclamation operation activities, such as assessing environmental effects, and planning mine closure and reclamation strategies.

Drone swarm technologies facilitate the monitoring of mine reclamation, such as providing a continuous 3D contour map of specific sites. Nonetheless, drone swarm technologies have significant benefits, such as enhanced accessibility and enhanced safety when utilized in tiny clusters. Both Ascot Resources (Canada) and the Chelopech mine (Bulgaria) utilize Exyns A3R drones equipped with LiDAR and SLAM technology for real-time mapping, enhancing operating flexibility in challenging conditions. However, dependence on centralized control systems and high deployment expenses have constrained their widespread adoption [103,104]. The available technology, such as drone swarm technologies, photogrammetry, computer vision, AI, and MR, can be used for the operation, continuous monitoring, and precise management of mine closure and reclamation.

6. How Is This Study Beneficial for Monitoring the Reclamation of Open-Pit Mines

Drones have broad applications in topographic mapping, monitoring landform alterations, evaluating land degradation, ecological surveillance, vegetation recovery, water runoff, erosion analysis, geological risk assessment, pollution detection, reclamation, and ecological restoration [12]. This helps mine administrators to meet environmental regulations and demonstrate compliance with post-mining land-use and rehabilitation standards. The monitoring of open-pit mine reclamation using drone technology aims to control the environmental footprint of this industry to achieve a more sustainable future.

Traditional ground surveys require substantial labor and are time-consuming. Drones can quickly survey extensive regions and deliver high-resolution images and 3D models; their low operational expenses, excellent accuracy, and remarkable time savings establish UAVs as a dependable means of data collection [12]. The use of UAVs for this purpose can provide researchers with rich basic data and achieve synchronous monitoring for the mine reclamation area. Drone-based survey eliminates the need for field personnel to access hazardous or unstable areas of the mine.

Drone technology allows for frequent and repeatable data collection, enabling real-time tracking of reclamation operations. This supports adaptive management and timely interventions if any part of the reclamation is not meeting the desired outcomes.

As illustrated in Figure 22, most of the relevant studies have concentrated on 3D modelling and terrain mapping, along with monitoring the ecology, geological risks, and vegetation coverage. Figure 22b demonstrates the significant potential of UAVs for both small- and large-scale land surveys [105]. The integration of drone-driven data (ortho-mosaic image, DSMs, DTMs, DEMs, etc.) into GIS and mine planning systems enhances the ability of mine planners, environmental engineers, and regulators to make informed decisions during mine closure phases.

Transparent, virtual visualization of successful reclamation using drone imagery can be shared with local communities, government bodies, and stakeholders, enhancing trust and supporting the social license to operate. This study promotes a modern cutting-edge computer vision approach to mine closure, aligning with global best practices and preparing open-pit mines for a more responsible and drone data-driven future.

7. Conclusions

This review study presented an examination of the research conducted over the past two decades on the utilization of drone technology in open-pit mine monitoring, encompassing 3D change detection, ground subsidence surveillance, and mine rehabilitation. A total of 1156 scholarly articles from WoS were identified, and their drone-related content was analyzed for systematic evaluation.

These evaluations revealed that contemporary drone applications in open-pit mining involve ecological rehabilitation, tracking, and ground settlement observation during reclamation. Among the current uses of drone technology, topographic mapping in open-pit mines has seen the widest adoption. The predominant drone configurations and sensor-derived data in mining were rotary-wing platforms and digital-camera-captured imagery/video, respectively.

Advancements in drone systems have enabled their active deployment in open-pit closure planning, environmental surveillance, land/resource assessment, and other scientific domains. This work presents findings from applications leveraging advanced drone platforms and sensors for open-pit mine rehabilitation zone monitoring, employing close-range photogrammetry and computer vision techniques for oversight and management. This review study highlights the potential of integrating drone and computer vision technologies to develop a monitoring framework, and proposes a resilient, cost-effective system for the surveillance of open-pit mine land rehabilitation. Drone applications in mine monitoring are experiencing accelerated growth. Selecting appropriate drones, deployment strategies, and sensors based on specific operational requirements represents a pivotal challenge in mine rehabilitation research. This carries substantial academic relevance and economic merit for establishing precise mine location/survey data, enabling dynamic observation, comprehending open-pit mine restoration, and advancing sustainable development.