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What are the main findings? Satellite observations have advanced from coarse (~10 km) to fine (25 m) resolution, greatly improving plume detection and narrowing gaps between bottom-up and top-down methane estimates. Significant uncertainties persist due to wind variability, terrain heterogeneity, retrieval-algorithm limitations, and limited temporal sampling, leading to inconsistent quantification, especially for diffuse surface coal-mine emissions. What are the implications of the main findings? Reliable site-level methane estimates require integrating satellites with ground and aerial validation, improved atmospheric modelling, and machine-learning methods that address diffuse and variable coal mine emissions. Reducing these uncertainties is essential for accurate reporting and effective mitigation in coal mining regions, thereby strengthening global climate action. Methane emissions from coal mines, especially surface operations, are spatially diffuse, presenting significant challenges for accurate quantification. Satellites such as TROPOMI, GHGSat, PRISMA, GaoFen-5, and GOSAT have been extensively used for detecting methane emissions at various scales, from individual point sources to regional and global assessments. Despite various advancements, methane quantification via satellite observations remains subject to several challenges. Various quantification methods for the same observation can produce variable results. Also, meteorological conditions, terrain complexity, and surface heterogeneity introduce uncertainties in emission estimates. The selection of wind speed and direction, along with retrieval-algorithm limitations, can lead to significant discrepancies in reported emissions. Additionally, satellite-based observations capture emissions only at specific overpass times, which may introduce temporal uncertainties compared to inventories derived from continuous emission estimations. This study provides a comprehensive review of satellite-based coal mine methane (CMM) monitoring, evaluating current methodologies, their limitations, and recent technological advancements. We discussed the potential of emerging machine-learning techniques, improved atmospheric modelling, and integrated observational approaches to enhance methane emission quantification. By refining satellite-based monitoring techniques and addressing existing challenges, this research will support the development of more accurate emission inventories and effective mitigation strategies for the coal mining sector.

In recent years, the fusion of camera data with LiDAR measurements has emerged as a powerful approach to enhance spatial understanding. This study introduces a novel, hardware-agnostic methodology that generates colourised point clouds from mechanical LiDAR using multiple camera inputs, providing complete 360-degree coverage. The primary innovation lies in its robustness under low-light conditions, achieved through the integration of a low-light image enhancement module within the fusion pipeline. The system requires initial calibration to determine intrinsic camera parameters, followed by automatic computation of the geometric transformation between the LiDAR and cameras—removing the need for specialised calibration targets and streamlining the setup. The data processing framework uses colour correction to ensure uniformity across camera feeds before fusion. The algorithm was tested using a Velodyne Puck Hi-Res LiDAR and a four-camera configuration. The optimised software achieved real-time performance and reliable colourisation even under very low illumination, successfully recovering scene details that would otherwise remain undetectable.

Rock bolts are crucial components in the subterranean support systems in underground mines that provide adequate structural reinforcement to the rock mass to prevent unforeseen hazards like rockfalls. This makes frequent assessments of such bolts critical for maintaining rock mass stability and minimising risks in underground mining operations. Where manual surveying of rock bolts is challenging due to the low-light conditions in the underground mines and the time-intensive nature of the process, automated detection of rock bolts serves as a plausible solution. To that end, this study focuses on the automatic identification of rock bolts within medium- to large-scale 3D point clouds obtained from underground mines using mobile laser scanners. Existing techniques for automated rock bolt identification primarily rely on feature engineering and traditional machine learning approaches. However, such techniques lack robustness as these point clouds present several challenges due to data noise, varying environments, and complex surrounding structures. Moreover, the target rock bolts are extremely small objects within large-scale point clouds and are often partially obscured due to the application of reinforcement shotcrete. Addressing these challenges, this paper proposes an approach termed DeepBolt, which employs a novel two-stage deep learning architecture specifically designed for handling severe class imbalance for the automatic and efficient identification of rock bolts in complex 3D point clouds. The proposed method surpasses state-of-the-art semantic segmentation models by up to 42.5% in Intersection over Union (IoU) for rock bolt points. Additionally, it outperforms existing rock bolt identification techniques, achieving a 96.41% precision and 96.96% recall in classifying rock bolts, demonstrating its robustness and effectiveness in complex underground environments.

Identification of optimal spectral bands often involves collecting in-field spectral signatures followed by thorough analysis. Such rigorous field sampling exercises are tedious, cumbersome, and often impractical on challenging terrain, which is a limiting factor for programmable hyperspectral sensors mounted on unmanned aerial vehicles (UAV-hyperspectral systems), requiring a pre-selection of optimal bands when mapping new environments with new target classes with unknown spectra. An innovative workflow has been designed and implemented to simplify the process of in-field spectral sampling and its realtime analysis for the identification of optimal spectral wavelengths. The band selection optimization workflow involves particle swarm optimization with minimum estimated abundance covariance (PSO-MEAC) for the identification of a set of bands most appropriate for UAV-hyperspectral imaging, in a given environment. The criterion function, MEAC, greatly simplifies the in-field spectral data acquisition process by requiring a few target class signatures and not requiring extensive training samples for each class. The metaheuristic method was tested on an experimental site with diversity in vegetation species and communities. The optimal set of bands were found to suitably capture the spectral variations between target vegetation species and communities. The approach streamlines the pre-tuning of wavelengths in programmable hyperspectral sensors in mapping applications. This will additionally reduce the total flight time in UAV-hyperspectral imaging, as obtaining information for an optimal subset of wavelengths is more efficient, and requires less data storage and computational resources for post-processing the data.

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.

Surface coal development activities include mining and ecological restoration, which significantly impact regional carbon sinks. Quantifying the dynamic impacts on carbon sequestration in vegetation (VCS) during coal development activities has been challenging. Here, we provided a novel approach to assess the dynamics of VCS affected by large-scale surface coal mining and subsequent restoration. This approach effectively overcomes the limitations imposed by the lack of finer scale and long-time series data through scale transformation. We found that mining activities directly decreased VCS by 384.63 Gg CO 2 , while restoration activities directly increased 192.51 Gg CO 2 between 2001 and 2022. As of 2022, the deficit in VCS at the mining areas still had 1966.7 Gg CO 2 . The study highlights that complete restoration requires compensating not only for the loss in the year of destruction but also for the ongoing accumulation of losses throughout the mining lifecycle. The findings deepen insights into the intricate relationship between coal resource development and ecological environmental protection.

Application of drone technology combined with LiDAR and Virtual Reality/Augmented Reality in surface mining operational optimization is growing on a high trajectory. The mining sector has demonstrated increased interest in using drones for everyday tasks such as bench face mapping, dump planning, dragline dump disposal as per balance diagram in surface mining. One of the key requirements in dragline mining is the application of 3-dimensional mapping of the dragline dump area and dump space management for the dragline dump in a safe and efficient manner. The drone can assist in judicious disposal of a dump near the dragline bench mining keeping in mind the available dump/pit space and slope stability requirements. This research article presents a review of drone technology and how LiDAR and Virtual Reality/Augmented Reality Technology based on cloud computing architecture can accelerate the mine planning activity for dragline dump disposal for a simple side cast mining method. Furthermore, it discusses the current applications of AI-driven 3D computer vision techniques in automating data analytics with point clouds for the extraction of terrain parameters and plan efficient dump disposal strategies by proper positioning of dragline in a large surface mine.

Mine waste dumps consist of individual, blob-like spoil piles, each with unique geological and geotechnical attributes that contribute to the overall stability of the dump. Manually characterising these individual spoil piles presents challenges due to issues of accessibility, safety risks, and time consumption. Analysis of remotely acquired images, through object-based classification, offers a promising solution for the effective identification and characterisation of individual spoil piles. However, object-based classification’s effectiveness hinges on segmentation, an aspect often overlooked in spoil pile analysis. Therefore, this study aims to identify and compare various segmentation approaches to pave the way for image-based spoil characterisation. A comparative analysis is conducted between traditional segmentation approaches and those rooted in deep learning methodologies. Among the diverse segmentation approaches evaluated, the morphology-based deep learning segmentation approach, Segment Anything Model (SAM), exhibited superior performance compared to other approaches. This outcome underscores the effectiveness of incorporating morphological data and deep learning techniques for accurate and efficient segmentation of spoil pile. The findings of this study provide valuable insights into the optimisation of segmentation strategies, thereby contributing to the application of image-based monitoring of spoil piles and promoting the sustainable and hazard free management of mine dump environments.

Near earth sensing from uncrewed aerial vehicles or UAVs has emerged as a potential approach for fine-scale environmental monitoring. These systems provide a cost-effective and repeatable means to acquire remotely sensed images in unprecedented spatial detail and a high signal-to-noise ratio. It is increasingly possible to obtain both physiochemical and structural insights into the environment using state-of-art light detection and ranging (LiDAR) sensors integrated onto UAVs. Monitoring sensitive environments, such as swamp vegetation in longwall mining areas, is essential yet challenging due to their inherent complexities. Current practices for monitoring these remote and challenging environments are primarily ground-based. This is partly due to an absent framework and challenges of using UAV-based sensor systems in monitoring such sensitive environments. This research addresses the related challenges in developing a LiDAR system, including a workflow for mapping and potentially monitoring highly heterogeneous and complex environments. This involves amalgamating several design components, including hardware integration, calibration of sensors, mission planning, and developing a processing chain to generate usable datasets. It also includes the creation of new methodologies and processing routines to establish a pipeline for efficient data retrieval and generation of usable products. The designed systems and methods were applied to a peat swamp environment to obtain an accurate geo-spatialised LiDAR point cloud. Performance of the LiDAR data was tested against ground-based measurements on various aspects, including visual assessment for generation LiDAR metrices maps, canopy height model, and fine-scale mapping.

Coal is heterogeneous in nature, and thus the characterization of coal is essential before its use for a specific purpose. Thus, the current study aims to develop a machine vision system for automated coal characterizations. The model was calibrated using 80 image samples that are captured for different coal samples in different angles. All the images were captured in RGB color space and converted into five other color spaces (HSI, CMYK, Lab, xyz, Gray) for feature extraction. The intensity component image of HSI color space was further transformed into four frequency components (discrete cosine transform, discrete wavelet transform, discrete Fourier transform, and Gabor filter) for the texture features extraction. A total of 280 image features was extracted and optimized using a step-wise linear regression-based algorithm for model development. The datasets of the optimized features were used as an input for the model, and their respective coal characteristics (analyzed in the laboratory) were used as outputs of the model. The R -squared values were found to be 0.89, 0.92, 0.92, and 0.84, respectively, for fixed carbon, ash content, volatile matter, and moisture content. The performance of the proposed artificial neural network model was also compared with the performances of performances of Gaussian process regression, support vector regression, and radial basis neural network models. The study demonstrates the potential of the machine vision system in automated coal characterization.