{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T20:06:21Z","timestamp":1790366781918,"version":"4.1.0"},"reference-count":33,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T00:00:00Z","timestamp":1762732800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Hubei Provincial Department of Education, China","award":["2023269"],"award-info":[{"award-number":["2023269"]}]},{"name":"Hubei Provincial Department of Education, China","award":["2018289"],"award-info":[{"award-number":["2018289"]}]},{"name":"College Students\u2019 Innovative Entrepreneurial Training Plan Program","award":["Yz2024019"],"award-info":[{"award-number":["Yz2024019"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>High-resolution lithological mapping of outcrops is fundamental for reservoir characterization and petroleum geology, yet distinguishing lithologies with subtle petrophysical contrasts remains a major challenge. This study proposes MC-H-Geo, a multi-scale contextual hierarchical framework for automated lithology classification from terrestrial laser scanning (TLS) point clouds. The framework integrates three modules: (i) a multi-scale contextual feature engine that extracts spectral, geometric, and textural descriptors across local and stratigraphic contexts, enhanced by cross-scale differentials to capture stratigraphic variability; (ii) a gated expert classifier with task-adaptive feature subsets for hierarchical vegetation\u2013rock and intra-rock discrimination; and (iii) a two-step geological post-processing procedure that enforces stratigraphic continuity through Z-axis correction and neighborhood smoothing. Experiments on the Qianwangjiahe outcrop (Ordos Basin, China) demonstrate state-of-the-art performance (OA = 94.3%, Macro F1 = 0.944), outperforming PointNet++ (77.1%), SG-RFGeo (74.2%), and XGBoost (61.7%). Error analysis reveals that residual sandstone\u2013vegetation confusion results from feature aliasing in weathered zones, highlighting the intrinsic limitations of TLS-only data. Overall, MC-H-Geo establishes an advanced framework for fine-grained lithological mapping and identifies multi-sensor data fusion as a promising pathway toward robust, geologically consistent outcrop interpretation.<\/jats:p>","DOI":"10.3390\/s25226859","type":"journal-article","created":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T15:07:31Z","timestamp":1762787251000},"page":"6859","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["MC-H-Geo: A Multi-Scale Contextual Hierarchical Framework for Fine-Grained Lithology Classification"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-4793-6527","authenticated-orcid":false,"given":"Lang","family":"Liu","sequence":"first","affiliation":[{"name":"School of Geosciences, Yangtze University, Wuhan 430100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-8337-7898","authenticated-orcid":false,"given":"Yanlin","family":"Shao","sequence":"additional","affiliation":[{"name":"School of Geosciences, Yangtze University, Wuhan 430100, China"},{"name":"Hubei Provincial Engineering Research Center for Unconventional Oil & Gas Geology and Engineering, Wuhan 430100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaxiong","family":"Shao","sequence":"additional","affiliation":[{"name":"Center for Governmental Studies, Northern Illinois University, DeKalb, IL 60115, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peijin","family":"Li","sequence":"additional","affiliation":[{"name":"School of Geosciences, Yangtze University, Wuhan 430100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingqing","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Geosciences, Yangtze University, Wuhan 430100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Zeng","sequence":"additional","affiliation":[{"name":"School of Geosciences, Yangtze University, Wuhan 430100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6511205","DOI":"10.1109\/LGRS.2022.3179623","article-title":"Intelligent On-Site Lithology Identification Based on Deep Learning of Rock Images and Elemental Data","volume":"19","author":"Xu","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.marpetgeo.2017.12.024","article-title":"Investigation of Pore Structure and Petrophysical Property in Tight Sandstones","volume":"91","author":"Lai","year":"2018","journal-title":"Mar. Pet. Geol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1016\/j.earscirev.2017.12.003","article-title":"A Review on Pore Structure Characterization in Tight Sandstones","volume":"177","author":"Lai","year":"2018","journal-title":"Earth-Sci. Rev."},{"key":"ref_4","first-page":"841","article-title":"Diagenetic Responses to Delta Front-Lacustrine Depositional Microfacies and Implications for Tight Reservoir Quality Differences in the Yanchang Formation, Western Ordos Basin","volume":"39","author":"Wang","year":"2021","journal-title":"Acta Sediment. Sin."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Kaartinen, E., Dunphy, K., and Sadhu, A. (2022). LiDAR-Based Structural Health Monitoring: Applications in Civil Infrastructure Systems. Sensors, 22.","DOI":"10.3390\/s22124610"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"166","DOI":"10.2110\/jsr.2005.013","article-title":"Digital Outcrop Models: Applications of Terrestrial Scanning Lidar Technology in Stratigraphic Modeling","volume":"75","author":"Bellian","year":"2005","journal-title":"J. Sediment. Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.isprsjprs.2013.12.004","article-title":"Application of Multispectral LiDAR to Automated Virtual Outcrop Geology","volume":"88","author":"Hartzell","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Shao, Y.L., Li, P.J., Jing, R., Shao, Y.X., Liu, L., Zhao, K.P., Gan, B.Q., Duan, X.L., and Li, L.F. (2025). A Machine Learning-Based Method for Lithology Identification of Outcrops Using TLS-Derived Spectral and Geometric Features. Remote Sens., 17.","DOI":"10.3390\/rs17142434"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Quan, Y.Z., Wang, Z.P., Peng, J.B., Xue, Y.G., Zang, M.D., Lu, C.M., and Ning, R.H. (2025). Cyclic Wetting\u2013Drying Driven Geological Gene Changes and Multi-scale Degradation Mechanisms of the Neogene Red-Bed Sandstone in the Guide Basin, NE Tibetan Plateau. Rock Mech. Rock Eng., 1\u201317.","DOI":"10.1007\/s00603-025-04746-8"},{"key":"ref_10","unstructured":"Pfeifer, N., H\u00f6fle, B., Briese, C., Rutzinger, M., and Haring, A. (2008, January 3\u201311). Analysis of the Backscattered Energy in Terrestrial Laser Scanning Data. Proceedings of the XXI Congress: Silk Road for Information from Imagery, Beijing, China."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1016\/j.ecss.2008.02.003","article-title":"Coastal and estuarine habitat mapping, using LIDAR height and intensity and multi-spectral imagery","volume":"78","author":"Chust","year":"2008","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1016\/j.rse.2016.08.018","article-title":"Beyond 3-D: The New Spectrum of Lidar Applications for Earth and Ecological Sciences","volume":"186","author":"Eitel","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"522","DOI":"10.1016\/j.isprsjprs.2009.03.003","article-title":"Discrimination between Marls and Limestones Using Intensity Data from Terrestrial Laser Scanner","volume":"64","author":"Franceschi","year":"2009","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.cageo.2014.03.014","article-title":"A New Approach for Semi-Automatic Rock Mass Joints Recognition from 3D Point Clouds","volume":"68","author":"Riquelme","year":"2014","journal-title":"Comput. Geosci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.isprsjprs.2012.01.006","article-title":"3D Terrestrial Lidar Data Classification of Complex Natural Scenes Using a Multi-Scale Dimensionality Criterion: Applications in Geomorphology","volume":"68","author":"Brodu","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.enggeo.2018.05.007","article-title":"Automated Measurements of Discontinuity Geometric Properties from a 3D-Point Cloud Based on a Modified Region Growing Algorithm","volume":"242","author":"Ge","year":"2018","journal-title":"Eng. Geol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"105326","DOI":"10.1016\/j.enggeo.2019.105326","article-title":"Classification Methods for Point Clouds in Rock Slope Monitoring: A Novel Machine Learning Approach and Comparative Analysis","volume":"263","author":"Weidner","year":"2019","journal-title":"Eng. Geol."},{"key":"ref_18","unstructured":"Qi, C.R., Yi, L., Su, H., and Guibas, L.J. (2019, January 16\u201320). PointNet++: Deep hierarchical feature learning on point sets in a metric space. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Liu, Y., Fan, B., Xiang, S., and Pan, C. (2019, January 16\u201320). Relation-Shape Convolutional Neural Network for Point Cloud Analysis. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00910"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.isprsjprs.2015.12.004","article-title":"Correction of Terrestrial LiDAR Intensity Channel Using Oren\u2013Nayar Reflectance Model: An Application to Lithological Differentiation","volume":"113","author":"Carrea","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3005","DOI":"10.1007\/s00603-018-1519-9","article-title":"Automatic Mapping of Discontinuity Persistence on Rock Masses Using 3D Point Clouds","volume":"51","author":"Riquelme","year":"2018","journal-title":"Rock Mech. Rock Eng."},{"key":"ref_22","first-page":"91","article-title":"Semantic3D.Net: A New Large-Scale Point Cloud Classification Benchmark","volume":"4","author":"Hackel","year":"2017","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_23","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":"ref_24","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.gr.2022.07.014","article-title":"Long-Term Phanerozoic Global Mean Sea Level: Insights from Strontium Isotope Variations and Estimates of Continental Glaciation","volume":"111","author":"Scotese","year":"2022","journal-title":"Gondwana Res."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.cageo.2013.01.018","article-title":"Terrestrial Lidar and Hyperspectral Data Fusion Products for Geological Outcrop Analysis","volume":"54","author":"Buckley","year":"2013","journal-title":"Comput. Geosci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.gmod.2012.09.001","article-title":"Complete Residential Urban Area Reconstruction from Dense Aerial LiDAR Point Clouds","volume":"75","author":"Zhou","year":"2013","journal-title":"Graph. Models"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"103969","DOI":"10.1016\/j.earscirev.2022.103969","article-title":"Smartphone Assisted Fieldwork: Towards the Digital Transition of Geoscience Fieldwork Using LiDAR-Equipped iPhones","volume":"227","author":"Tavani","year":"2022","journal-title":"Earth-Sci. Rev."},{"key":"ref_28","first-page":"55","article-title":"Analysis of Spectral Absorption Features in Hyperspectral Imagery","volume":"5","year":"2004","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1007\/s00603-009-0048-y","article-title":"Application of Rock Mass Characterization for Determining the Mechanical Properties of Rock Mass: A Comparative Study","volume":"43","author":"Hashemi","year":"2010","journal-title":"Rock Mech. Rock Eng."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"105131","DOI":"10.1016\/j.enggeo.2019.05.008","article-title":"Automatic Characterization of Rock Mass Discontinuities Using 3D Point Clouds","volume":"259","author":"Li","year":"2019","journal-title":"Eng. Geol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural Features for Image Classification","volume":"SMC-3","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1150","DOI":"10.1109\/LSP.2021.3088059","article-title":"Color and Geometry Texture Descriptors for Point-Cloud Quality Assessment","volume":"28","author":"Diniz","year":"2021","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.jvcir.2019.03.005","article-title":"Frequency Domain Point Cloud Registration Based on the Fourier Transform","volume":"61","author":"Zhang","year":"2019","journal-title":"J. Vis. Commun. Image Represent."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/22\/6859\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T15:11:29Z","timestamp":1762787489000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/22\/6859"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,10]]},"references-count":33,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["s25226859"],"URL":"https:\/\/doi.org\/10.3390\/s25226859","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,10]]}}}