{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T11:39:09Z","timestamp":1782301149314,"version":"3.54.5"},"reference-count":30,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100010244","name":"Science Foundation of China University of Petroleum Beijing","doi-asserted-by":"publisher","award":["2462024YJRC013"],"award-info":[{"award-number":["2462024YJRC013"]}],"id":[{"id":"10.13039\/501100010244","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42002134"],"award-info":[{"award-number":["42002134"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers &amp; Geosciences"],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1016\/j.cageo.2026.106136","type":"journal-article","created":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T06:27:21Z","timestamp":1769927241000},"page":"106136","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"C","title":["Intelligent characterization of fractures using 3D point clouds of outcrops generated from 2D photos"],"prefix":"10.1016","volume":"210","author":[{"given":"Shaoqun","family":"Dong","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-5597-5649","authenticated-orcid":false,"given":"Kaifeng","family":"Fu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leting","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lianbo","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.cageo.2026.106136_bib1","first-page":"1","article-title":"Deep learning for seismic data compression in distributed acoustic sensing","volume":"63","author":"Chen","year":"2025","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"10.1016\/j.cageo.2026.106136_bib2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.sedgeo.2017.03.013","article-title":"Using unmanned aerial vehicles and structure-from-motion photogrammetry to characterize sedimentary outcrops: an example from the Morrison Formation, Utah, USA","volume":"354","author":"Chesley","year":"2017","journal-title":"Sediment. Geol."},{"key":"10.1016\/j.cageo.2026.106136_bib3","doi-asserted-by":"crossref","DOI":"10.1016\/j.cageo.2022.105241","article-title":"A critical review of discontinuity plane extraction from 3D point cloud data of rock mass surfaces","volume":"169","author":"Daghigh","year":"2022","journal-title":"Comput. Geosci."},{"issue":"5","key":"10.1016\/j.cageo.2026.106136_bib4","first-page":"1302","article-title":"Principle and implementation of discrete fracture network modeling controlled by fracture density","volume":"64","author":"Dong","year":"2018","journal-title":"Geol. Rev."},{"issue":"10","key":"10.1016\/j.cageo.2026.106136_bib5","doi-asserted-by":"crossref","first-page":"1584","DOI":"10.1016\/j.cageo.2011.03.007","article-title":"Supervised identification and reconstruction of near-planar geological surfaces from terrestrial laser scanning","volume":"37","author":"Garc\u00eda-Sell\u00e9s","year":"2011","journal-title":"Comput. Geosci."},{"issue":"3","key":"10.1016\/j.cageo.2026.106136_bib6","doi-asserted-by":"crossref","first-page":"1705","DOI":"10.1007\/s00603-021-02748-w","article-title":"Rock discontinuities identification from 3D point clouds using Artificial Neural Network","volume":"55","author":"Ge","year":"2022","journal-title":"Rock Mech. Rock Eng."},{"issue":"4","key":"10.1016\/j.cageo.2026.106136_bib7","doi-asserted-by":"crossref","DOI":"10.1144\/qjegh2023-012","article-title":"Rock joint detection from 3D point clouds based on colour space","volume":"56","author":"Ge","year":"2023","journal-title":"Q. J. Eng. Geol. Hydrogeol."},{"key":"10.1016\/j.cageo.2026.106136_bib8","doi-asserted-by":"crossref","DOI":"10.1016\/j.petrol.2021.108655","article-title":"Characterization, controlling factors and evolution of fracture effectiveness in shale oil reservoirs","volume":"203","author":"Gong","year":"2021","journal-title":"J. Petrol. Sci. Eng."},{"key":"10.1016\/j.cageo.2026.106136_bib9","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.cageo.2017.03.017","article-title":"Towards semi-automatic rock mass discontinuity orientation and set analysis from 3D point clouds","volume":"103","author":"Guo","year":"2017","journal-title":"Comput. Geosci."},{"key":"10.1016\/j.cageo.2026.106136_bib10","series-title":"The Segmentation of Point Clouds with K-Means and ANN (Artifical Neural Network)","first-page":"595","author":"Ku\u00e7ak","year":"2017"},{"issue":"2","key":"10.1016\/j.cageo.2026.106136_bib11","article-title":"FaultVitNet: a vision transformer assisted network for 3D fault segmentation","volume":"2","author":"Li","year":"2025","journal-title":"J. geophys. res., Mach. learn. comput."},{"issue":"11","key":"10.1016\/j.cageo.2026.106136_bib12","article-title":"A progress review on Solid\u2010State LiDAR and nanophotonics\u2010based LiDAR sensors","volume":"16","author":"Li","year":"2022","journal-title":"Laser Photonics Rev."},{"key":"10.1016\/j.cageo.2026.106136_bib13","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.compgeo.2014.11.004","article-title":"A fractal model for characterizing fluid flow in fractured rock masses based on randomly distributed rock fracture networks","volume":"65","author":"Liu","year":"2015","journal-title":"Comput. Geotech."},{"key":"10.1016\/j.cageo.2026.106136_bib14","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive image features from scale-invariant keypoints","volume":"60","author":"Lowe","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"10.1016\/j.cageo.2026.106136_bib15","first-page":"1","article-title":"Fast approximate nearest neighbors with automatic algorithm configuration","volume":"1","author":"Marius Muja","year":"2009","journal-title":"Proceedings of the Fourth International Conference on Computer Vision Theory and Applications VISIGRAPP -"},{"key":"10.1016\/j.cageo.2026.106136_bib16","doi-asserted-by":"crossref","DOI":"10.1016\/j.earscirev.2020.103260","article-title":"Virtual and digital outcrops in the petroleum industry: a systematic review","volume":"208","author":"Marques","year":"2020","journal-title":"Earth Sci. Rev."},{"issue":"6","key":"10.1016\/j.cageo.2026.106136_bib17","doi-asserted-by":"crossref","first-page":"1663","DOI":"10.3390\/s20061663","article-title":"Comparison of different remote sensing methods for 3D modeling of small rock outcrops","volume":"20","author":"Mikita","year":"2020","journal-title":"SENSORS-BASEL"},{"key":"10.1016\/j.cageo.2026.106136_bib18","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.cageo.2017.08.013","article-title":"Automatic extraction of blocks from 3D point clouds of fractured rock","volume":"109","author":"Na","year":"2017","journal-title":"Comput. Geosci."},{"key":"10.1016\/j.cageo.2026.106136_bib19","first-page":"807","article-title":"Rectified linear units improve restricted Boltzmann machines","volume":"2010","author":"Nair","year":"2010","journal-title":"ICML"},{"key":"10.1016\/j.cageo.2026.106136_bib20","series-title":"2016 IEEE Conference on Computer Vision and Pattern Recognition","first-page":"4104","article-title":"Structure-from-Motion revisited","author":"Sch\u00f6nberger","year":"2016"},{"issue":"4","key":"10.1016\/j.cageo.2026.106136_bib21","first-page":"415","article-title":"Quantified fracture (joint) clustering in Archean basement, Wyoming; application of the normalized correlation count method. Pet","volume":"25","author":"Wang","year":"2019","journal-title":"Geosci."},{"key":"10.1016\/j.cageo.2026.106136_bib22","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.cageo.2016.11.002","article-title":"A region-growing approach for automatic outcrop fracture extraction from a three-dimensional point cloud","volume":"99","author":"Wang","year":"2017","journal-title":"Comput. Geosci."},{"issue":"5","key":"10.1016\/j.cageo.2026.106136_bib23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3326362","article-title":"Dynamic graph CNN for learning on point clouds","volume":"38","author":"Wang","year":"2019","journal-title":"ACM T GRAPHIC"},{"key":"10.1016\/j.cageo.2026.106136_bib24","first-page":"486","article-title":"Applications of structure frommotion: a survey","volume":"14","author":"Wei","year":"2013","journal-title":"Comput. Electron."},{"key":"10.1016\/j.cageo.2026.106136_bib25","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1016\/j.isprsjprs.2015.01.016","article-title":"Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers","volume":"105","author":"Weinmann","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"10.1016\/j.cageo.2026.106136_bib26","doi-asserted-by":"crossref","DOI":"10.1016\/j.rineng.2025.105614","article-title":"3-D fracture network reconstruction and quantitative fractal analysis of subsurface rock fractures via integrated CT scanning and box-counting dimension methodology","volume":"26","author":"Wu","year":"2025","journal-title":"Results Eng."},{"key":"10.1016\/j.cageo.2026.106136_bib27","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1007\/978-3-030-01237-3_47","article-title":"MVSNet: depth inference for unstructured multi-view stereo","volume":"11212","author":"Yao","year":"2018","journal-title":"Lect. Notes Comput. Sci."},{"issue":"7","key":"10.1016\/j.cageo.2026.106136_bib28","doi-asserted-by":"crossref","first-page":"4873","DOI":"10.1007\/s00603-024-03804-x","article-title":"OCM: an intelligent recognition method of rock discontinuity based on optimal color mapping of 3D Point cloud via deep learning","volume":"57","author":"Zhang","year":"2024","journal-title":"Rock Mech. Rock Eng."},{"issue":"5","key":"10.1016\/j.cageo.2026.106136_bib29","doi-asserted-by":"crossref","first-page":"1248","DOI":"10.1007\/s11431-012-5129-6","article-title":"Determination of critical slip surface of fractured rock slopes based on fracture orientation data","volume":"56","author":"Zhang","year":"2013","journal-title":"Sci. China Technol. Sci."},{"key":"10.1016\/j.cageo.2026.106136_bib30","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijrmms.2023.105627","article-title":"An optimized fuzzy K-means clustering method for automated rock discontinuities extraction from point clouds","volume":"173","author":"Zhou","year":"2024","journal-title":"Int. J. Rock Mech. Min. Sci."}],"container-title":["Computers &amp; Geosciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0098300426000336?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0098300426000336?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T07:51:14Z","timestamp":1773993074000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0098300426000336"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4]]},"references-count":30,"alternative-id":["S0098300426000336"],"URL":"https:\/\/doi.org\/10.1016\/j.cageo.2026.106136","relation":{},"ISSN":["0098-3004"],"issn-type":[{"value":"0098-3004","type":"print"}],"subject":[],"published":{"date-parts":[[2026,4]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Intelligent characterization of fractures using 3D point clouds of outcrops generated from 2D photos","name":"articletitle","label":"Article Title"},{"value":"Computers & Geosciences","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.cageo.2026.106136","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"106136"}}