[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128547-en":3,"doc-seo-128547-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},128547,549768064622,"Anda","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Geotechnical Characterisation of Coal Spoil Piles Using High-Resolution Optical and Multispectral Data - A Machine Learning Approach","Geotechnical characterisation of coal spoil piles has often depended on field experts, creating hazards and long processing times. Segmentation and classification of very high-resolution remote-sensing imagery for irregular, heterogeneous mine dumps remain difficult. The proposed machine-learning pipeline combines morphology-based segmentation with spectral, textural, structural, and statistical feature extraction, then applies mRMR feature selection and ML classification. The resulting automated characterisation supports proactive dump stability assessment and improves stability models while advancing greener mining practices.","Boise State University  \nScholarWorks  \n\n| Geosciences Faculty Publications and Presentations | Department of Geosciences |\n| --- | --- |\n| 2-2024\u003Cbr>Geotechnical Characterisation of Coal Spoil Piles Using High-Resolution Optical and Multispectral Data: A Machine Learning Approach\u003Cbr>Sureka Thiruchittampalam University of New South Wales\u003Cbr>Bikram Pratap Banerjee\u003Cbr>University of Southern Queensland\u003Cbr>Nancy F. Glenn\u003Cbr>Boise State University\u003Cbr>Simit Raval\u003Cbr>University of New South Wales |  |\n\nPublication Information  \nThiruchittampalam, Sureka; Banerjee, Bikram Pratap; Glenn, Nancy F.; and Raval, Simit. (2024) .  \n\"Geotechnical Characterisation of Coal Spoil Piles Using High-Resolution Optical and Multispectral Data: A Machine Learning Approach\" . Engineering Geology, 329, 107406. [https://doi.org/10.1016/](https://doi.org/10.1016/)  \nj.enggeo.2024.107406  \nEngineering Geology 329 (2024) 107406  \nContents lists available at ScienceDirect  \nEngineering Geology  \njournal [homepage:](homepage: www.elsevier.com/locate/enggeo)[ www.elsevier.com/locate/enggeo](homepage: www.elsevier.com/locate/enggeo)  \n| Geotechnical characterisation of coal spoil piles using high-resolution optical and multispectral data: A machine learning approach\u003Cbr>Sureka Thiruchittampalama, b, Bikram Pratap Banerjee c, Nancy F. Glennd, Simit Ravala, *\u003Cbr>a School of Minerals and Energy Resources Engineering, University of New South Wales, Sydney, NSW 2052, Australia b Department of Earth Resources Engineering, University of Moratuwa, Moratuwa 01400, Sri Lanka\u003Cbr>c School of Surveying and Built Environment, University of Southern Queensland, Toowoomba, QLD 4350, Australia d Department of Geosciences, Boise State University, Boise, ID, USA |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords:\u003Cbr>Object-based image analysis Morphology-based segmentation Waste materials\u003Cbr>Mine dump\u003Cbr>High-resolution UAV images Shear strength parameters |  | Geotechnical characterisation of spoil piles has traditionally relied on the expertise of field specialists, which can be both hazardous and time-consuming. Although unmanned aerial vehicles (UAV) show promise as a remote sensing tool in various applications; accurately segmenting and classifying very high-resolution remote sensing images of heterogeneous terrains, such as mining spoil piles with irregular morphologies, presents significant challenges. The proposed method adopts a robust approach that combines morphology-based segmentation, as well as spectral, textural, structural, and statistical feature extraction techniques to overcome the difficulties associated with spoil pile characterisation. Additionally, it incorporates minimum redundancy maximum relevance (mRMR) based feature selection and machine learning-based classification. This automated characterisation will serve as a proactive tool for dump stability assessment, providing crucial data for improved stability models and contributing to a greener and more responsible mining industry . |\n\n1. Introduction  \nCoal mining, including the construction of coal spoil dumps, poses significant environmental and safety challenges when not managed properly. The geotechnical characterisation of coal spoil is essential for monitoring and evaluating environmental impacts and safety concerns, as well as guiding effective land restoration efforts (Zevgolis et al., 2021). This data also supports policymaking, raises awareness about coal mining effects, and facilitates informed decision-making. However, the irregular deposition processes of spoil and inadequate compaction during placement can introduce complexities and uncertainties in spoil dump behavior (Masoudian et al., 2019).  \nAnalysing the spatial arrangement of characterised coal spoil is crucial, but it’s challenging due to variations in properties between mines, influenced by factors like ore type and mining techniques (Lottermoser, 2007). Traditional manual field methods for spoil c","cbCainyog063QmS7","https://ap.wps.com/l/cbCainyog063QmS7","pdf",6451464,1,21,"English","en",105,"# Introduction\n## Motivation and challenges in coal spoil characterisation\n## Role of UAVs and remote sensing\n## Sensor selection: RGB vs multispectral and calibration considerations\n## Sensor challenges across the workflow\n## Objective: automated characterisation for stability assessment","[{\"question\":\"Why are traditional field methods for coal spoil characterisation problematic?\",\"answer\":\"They are time-consuming and can be hazardous for field specialists. Irregular deposition and variable compaction also increase uncertainty in spoil behavior.\"},{\"question\":\"What makes segmentation of coal spoil piles from remote sensing difficult?\",\"answer\":\"Spoil piles have irregular morphologies and heterogeneous terrain, which complicates accurate segmentation and classification in very high-resolution imagery.\"},{\"question\":\"How does the proposed approach automate coal spoil pile characterisation?\",\"answer\":\"It uses morphology-based segmentation and extracts spectral, textural, structural, and statistical features, then performs mRMR feature selection and machine-learning classification.\"}]","Geotechnical Characterisation of Coal Spoil Piles Using High-Resolution Optical and Multispectral Data - A Machine Learning Approach | PDF",1786001663,53,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"geotechnical-characterisation-of-coal-spoil-piles-using-high-resolution-optical-and-multispectral-data-a-machine-learning-approach-128547","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/geotechnical-characterisation-of-coal-spoil-piles-using-high-resolution-optical-and-multispectral-data-a-machine-learning-approach-128547/128547/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-06",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why are traditional field methods for coal spoil characterisation problematic?","Question",{"text":76,"@type":77},"They are time-consuming and can be hazardous for field specialists. Irregular deposition and variable compaction also increase uncertainty in spoil behavior.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What makes segmentation of coal spoil piles from remote sensing difficult?",{"text":81,"@type":77},"Spoil piles have irregular morphologies and heterogeneous terrain, which complicates accurate segmentation and classification in very high-resolution imagery.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the proposed approach automate coal spoil pile characterisation?",{"text":85,"@type":77},"It uses morphology-based segmentation and extracts spectral, textural, structural, and statistical features, then performs mRMR feature selection and machine-learning classification.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]