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The workflow automatically tunes its parameters from the statistics of each input scene, eliminating manual parameter tuning. For instance, it sets the ICP correspondence distance and the clustering threshold without user input. Additionally, our method integrates a coarse\u2010to\u2010fine registration strategy, robust change detection, and precise volumetric estimation based on digital elevation models. Experiments on simulated mining datasets show our method remains robust under heavy noise and misalignment, with volume errors consistently below . A field pilot study at a limestone quarry further underscores its practical reliability and operational robustness. This research provides a precise, automated solution for real\u2010time mining monitoring, effectively advancing sustainable and intelligent mining practices. Source code and datasets are publicly available at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/deemoe404\/volcal_baseline\">github.com\/deemoe404\/volcal_baseline<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1049\/ipr2.70130","type":"journal-article","created":{"date-parts":[[2025,6,16]],"date-time":"2025-06-16T09:19:26Z","timestamp":1750065566000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Data\u2010Driven Solution for Large\u2010Scale Open\u2010Pit Mines Excavation Monitoring Based on 3D Point Cloud"],"prefix":"10.1049","volume":"19","author":[{"given":"Taiming","family":"He","sequence":"first","affiliation":[{"name":"School of Automation Engineering University of Electronic Science and Technology of China Chengdu China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-4213-5418","authenticated-orcid":false,"given":"Jiasui","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Automation Engineering University of Electronic Science and Technology of China Chengdu China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lu","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Automation Engineering University of Electronic Science and Technology of China Chengdu China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"265","published-online":{"date-parts":[[2025,6,16]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12665-017-6409-z"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.3390\/rs11060606"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2022.112364"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.3390\/rs15205006"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00138-024-01510-w"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.3390\/rs13183564"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.15607\/RSS.2009.V.021"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2013.04.009"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.1002\/ppp.2004"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2025.130533"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2014.06.002"},{"key":"e_1_2_11_13_1","unstructured":"M.Ester H.\u2010P.Kriegel J.Sander andX.Xu \u201cA Density\u2010Based Algorithm for Discovering Clusters in Large Spatial Databases With Noise \u201d inProceedings of the Second International Conference on Knowledge Discovery and Data Mining KDD'96 (AAAI Press 1996) 226\u2013231."},{"key":"e_1_2_11_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2020.03.013"},{"key":"e_1_2_11_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2018.06.018"},{"key":"e_1_2_11_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2017.06.012"},{"key":"e_1_2_11_17_1","doi-asserted-by":"crossref","unstructured":"L.Winiwarter K.Anders D.Wujanz andB.H\u00f6fle \u201cInfluence of Ranging Uncertainty of Terrestrial Laser Scanning on Change Detection in Topographic 3D Point Clouds \u201d inISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences V\u20102\u20102020 (2020) 789\u2013796.","DOI":"10.5194\/isprs-annals-V-2-2020-789-2020"},{"key":"e_1_2_11_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.optlaseng.2015.11.010"},{"key":"e_1_2_11_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.aej.2021.04.011"},{"key":"e_1_2_11_20_1","doi-asserted-by":"publisher","DOI":"10.3390\/rs12213522"},{"key":"e_1_2_11_21_1","doi-asserted-by":"crossref","unstructured":"J.Balado E.Gonz\u00e1lez E.Verbree L.D\u00edaz\u2010Vilari\u00f1o andH.Lorenzo \u201cAutomatic Detection and Characterization of Ground Occlusions in Urban Point Clouds From Mobile Laser Scanning Data \u201d inISPRS Annals of the Photogrammetry Remote Sensing and Spatial Information Sciences VI\u20104\/W1\u20102020 (2020) 13\u201320.","DOI":"10.5194\/isprs-annals-VI-4-W1-2020-13-2020"},{"key":"e_1_2_11_22_1","doi-asserted-by":"publisher","DOI":"10.3390\/su16072827"},{"key":"e_1_2_11_23_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-69368-6"},{"key":"e_1_2_11_24_1","doi-asserted-by":"publisher","DOI":"10.3390\/s22155706"},{"key":"e_1_2_11_25_1","unstructured":"V. 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