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

International Journal of Coal Science & Technology

Research

Bikram Pratap Banerjee¹  Nancy F. Glenn²  Sureka Thiruchittampalam³  Sarvesh Kumar Singh³  Simit Raval³ 

[1] Agriculture Victoria, Grains Innovation Park, 110 Natimuk Road, 3400, Horsham, VIC, Australia;Department of Geosciences, Boise State University, Boise, ID, USA;School of Minerals and Energy Resources Engineering, University of New South Wales, 2052, Sydney, NSW, Australia;

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

DOI  :  10.1007/s40789-023-00622-4

 received in 2022-08-09, accepted in 2023-08-03,  发布年份 2023

来源: Springer

【 摘 要 】

The structural integrity of mine dumps is crucial for mining operations to avoid adverse impacts on the triple bottom-line. Routine temporal assessments of coal mine dumps are a compliant requirement to ensure design reconciliation as spoil offloading continues over time. Generally, the conventional in-situ coal spoil characterisation is inefficient, laborious, hazardous, and prone to experts' observation biases. To this end, this study explores a novel approach to develop automated coal spoil characterisation using unmanned aerial vehicle (UAV) based optical remote sensing. The 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 workflow. The raw images were converted to an orthomosaic using structure from motion aided processing. Then, structural descriptors were computed per pixel to enhance feature modalities of the spoil materials. Finally, machine learning algorithms were employed with ground truth from experts as training and testing data to characterise spoil rapidly with minimal human intervention. The characterisation accuracies achieved from the proposed approach manifest a digital solution to address the limitations in the conventional characterisation approach.

【 授权许可】

CC BY   
© The Author(s) 2023

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