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

Article


Thiruchittampalam, Sureka, Singh, Sarvesh Kumar, Banerjee, Bikram Pratap, Glenn, Nancy F. and Raval, Simit. 2023. "Spoil characterisation using UAV‑based optical remote sensing in coal mine dumps." International Journal of Coal Science and Technology. 10 (1). https://doi.org/10.1007/s40789-023-00622-4
Article Title

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

ERA Journal ID210637
Article CategoryArticle
AuthorsThiruchittampalam, Sureka, Singh, Sarvesh Kumar, Banerjee, Bikram Pratap, Glenn, Nancy F. and Raval, Simit
Journal TitleInternational Journal of Coal Science and Technology
Journal Citation10 (1)
Article Number65
Number of Pages15
Year2023
Place of PublicationChina
ISSN2095-8293
2198-7823
Digital Object Identifier (DOI)https://doi.org/10.1007/s40789-023-00622-4
Web Address (URL)https://link.springer.com/article/10.1007/s40789-023-00622-4
Abstract

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.

KeywordsConsistency/relative density; Dimensionality reduction; Fabric structure; Lithology; Supervised learning algorithms
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020401304. Photogrammetry and remote sensing
401905. Mining engineering
Byline AffiliationsUniversity of New South Wales
Agriculture Victoria
Boise State University, United States
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