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Mapping Sensitive Vegetation Communities in Mining Eco-space using UAV-LiDAR

International Journal of Coal Science & Technology 2022年第3期9卷 185-200页

作者： Bikram Pratap Banerjee simit raval Agriculture Victoria Grains Innovation Park110 Natimuk Road3400 HorshamVICAustralia School of Minerals and Energy Resources Engineering University of New South Wales2052 SydneyNSWAustralia

Near earth sensing from uncrewed aerial vehicles or UAVs has emerged as a potential approach for fne-scale environmental *** systems provide a cost-efective and repeatable means to acquire remotely sensed images in unprecedented spatial detail and a high signal-to-noise *** 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 *** sensitive environments,such as swamp vegetation in longwall mining areas,is essential yet challenging due to their inherent *** practices for monitoring these remote and challenging environments are primarily *** is partly due to an absent framework and challenges of using UAV-based sensor systems in monitoring such sensitive *** research addresses the related challenges in developing a LiDAR system,including a workfow for mapping and potentially monitoring highly heterogeneous and complex *** involves amalgamating several design components,including hardware integration,calibration of sensors,mission planning,and developing a processing chain to generate usable *** also includes the creation of new methodologies and processing routines to establish a pipeline for efcient data retrieval and generation of usable *** designed systems and methods were applied to a peat swamp environment to obtain an accurate geo-spatialised LiDAR point *** 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 fne-scale mapping.

关键词： Upland swamps Mine surveying Monitoring Environment sustainability Drones and laser scanning

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A robust approach to identify roof bolts in 3D point cloud data captured from a mobile laser scanner

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International Journal of Mining Science and Technology 2021年第2期31卷 303-312页

作者： Sarvesh Kumar Singh simit raval Bikram Banerjee School of Minerals and Energy Resources Engineering University of New South WalesSydneyNSW 2052Australia Agriculture Victoria HorshamVIC 3400Australia

Roof bolts such as rock bolts and cable bolts provide structural support in underground *** assessment of these support structures is critical to maintain roof stability and minimise safety risks in underground *** study proposes a robust workflow to classify roof bolts in 3 D point cloud data and to generate maps of roof bolt density and *** workflow was evaluated for identifying roof bolts in an underground coal mine with suboptimal lighting and global navigation satellite system(GNSS)signals not *** approach is based on supervised classification using the multi-scale Canupo classifier coupled with a random sample consensus(RANSAC)shape detection algorithm to provide robust roof bolt *** issue of sparseness in point cloud data has been addressed through upsampling by using a moving least squares *** accuracy of roof bolt identification was measured by correct identification of roof bolts(true positives),unidentified roof bolts(false negatives),and falsely identified roof bolts(false positives)using correctness,completeness,and quality *** proposed workflow achieved correct identification of 89.27%of the roof bolts present in the test ***,considering the false positives and false negatives,the overall quality metric was reduced to 78.54%.

关键词： Mobile laser scanning Roof bolts Support system planning Roof monitoring Change detection Underground roadways

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A review of laser scanning for geological and geotechnical applications in underground mining

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International Journal of Mining Science and Technology 2023年第2期33卷 133-154页

作者： Sarvesh Kumar Singh Bikram Pratap Banerjee simit raval School of Minerals and Energy Resources Engineering University of New South WalesSydneyNSW 2052Australia Agriculture Victoria Grains Innovation ParkHorshamVIC 3400Australia

Laser scanning can provide timely assessments of mine sites despite adverse challenges in the operational *** there are several published articles on laser scanning,there is a need to review them in the context of underground mining *** this end,a holistic review of laser scanning is presented including progress in 3D scanning systems,data capture/processing techniques and primary applications in underground *** scanning technology has advanced significantly in terms of mobility and mapping,but there are constraints in coherent and consistent data collection at certain mines due to feature deficiency,dynamics,and environmental influences such as dust and *** suggest that laser scanning has matured over the years for change detection,clearance measurements and structure mapping ***,there is scope for improvements in lithology identification,surface parameter measurements,logistic tracking and autonomous *** scanning has the potential to provide real-time solutions but the lack of infrastructure in underground mines for data transfer,geodetic networking and processing capacity remain limiting ***,laser scanners are becoming an integral part of mine automation thanks to their affordability,accuracy and mobility,which should support their widespread usage in years to come.

关键词： Mine automation Point cloud Rock mass characterisation Change detection Data registration Georeferencing

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Design and development of a machine vision system using artificial neural network-based algorithm for automated coal characterization

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International Journal of Coal Science & Technology 2021年第4期8卷 737-755页

作者： Amit Kumar Gorai simit raval Ashok Kumar Patel Snehamoy Chatterjee Tarini Gautam Department of Mining Engineering National Institute of TechnologyRourkela 769008India School of Minerals and Energy Resources Engineering University of New South WalesSydneyNSW 2052Australia Department of Computer Science&Engineering C.V.Raman Global UniversityBhubaneswarOdisha 752054India Department of Geological and Mining Engineering and Science Michigan Technological UniversityHoughtonMI 49931USA

Coal is heterogeneous in nature,and thus the characterization of coal is essential before its use for a specific ***,the current study aims to develop a machine vision system for automated coal *** model was calibrated using 80 image samples that are captured for different coal samples in different *** the images were captured in RGB color space and converted into five other color spaces(HSI,CMYK,Lab,xyz,Gray)for feature *** 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 *** 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 *** 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 *** 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 *** study demonstrates the potential of the machine vision system in automated coal characterization.

关键词： Coal characterization Machine vision system Artificial neural network Gaussian process regression

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An investigation of machine learning techniques to estimate minimum horizontal stress magnitude from borehole breakout

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International Journal of Mining Science and Technology 2022年第5期32卷 1021-1029页

作者： Huasheng Lin Sarvesh Kumar Singh Zizhuo Xiang Won Hee Kang simit raval Joung Oh Ismet Canbulat Glencore Coal Assets Australia Mount Thorley 2330Australia School of Minerals and Energy Resources Engineering University of New South WalesSydney 2052Australia Centre for Infrastructure Engineering School of EngineeringWestern Sydney UniversityPenrith 2751Australia

Borehole breakout is a widely utilised phenomenon in horizontal stress orientation determination,and breakout geometrical parameters,such as width and depth,have been used to estimate both horizontal stress ***,the accuracy of minimum horizontal stress estimation from borehole breakout remains relatively low in comparison to maximum horizontal stress *** paper aims to compare and improve the minimum horizontal stress estimation via a number of machine learning(ML)regression techniques,including parametric and non-parametric models,which have rarely been *** models were trained based on 79 laboratory data from published literature and validated against 23 field data.A systematic bias was observed in the prediction for the validation dataset whenever the horizontal stress value exceeded the maximum value in the training ***,the pattern was captured,and the removal of systematic bias showed that the artificial neural network is capable of predicting the minimum horizontal stress with an average error rate of 10.16%and a root mean square error of 3.87 MPa when compared to actual values obtained through conventional in-situ measurement *** is a meaningful improvement considering the importance of in-situ stress knowledge for underground operations and the availability of borehole breakout data.

关键词： Borehole breakout In-situ stress estimation Comparative analysis Machine learning

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Spoil characterisation using UAV-based optical remote sensing in coal mine dumps

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International Journal of Coal Science & Technology 2023年第5期10卷 72-86页

作者： Sureka Thiruchittampalam Sarvesh Kumar Singh Bikram Pratap Banerjee Nancy F.Glenn simit raval School of Minerals and Energy Resources Engineering University of New South WalesSydneyNSW 2052Australia Agriculture Victoria Grains Innovation Park110 Natimuk RoadHorshamVIC 3400Australia Department of Geosciences Boise State UniversityBoiseIDUSA

The structural integrity of mine dumps is crucial for mining operations to avoid adverse impacts on the triple *** temporal assessments of coal mine dumps are a compliant requirement to ensure design reconciliation as spoil off-loading continues over ***,the conventional in-situ coal spoil characterisation is inefficient,laborious,hazardous,and prone to experts'observation *** this end,this study explores a novel approach to develop automated coal spoil characterisation using unmanned aerial vehicle(UAV)based optical remote *** textural and spectral properties of the high-resolution UAV images were utilised to derive lithology and geotechnical parameters(i.e.,fabric structure and relative density/consistency)in the proposed *** raw images were converted to an orthomosaic using structure from motion aided ***,structural descriptors were computed per pixel to enhance feature modalities of the spoil ***,machine learning algorithms were employed with ground truth from experts as training and testing data to characterise spoil rapidly with minimal human *** characterisation accuracies achieved from the proposed approach manifest a digital solution to address the limitations in the conventional characterisation approach.

关键词： Lithology Fabric structure Consistency/relative density Dimensionality reduction Supervised learning algorithms

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