{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T19:42:22Z","timestamp":1775072542561,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2024,6,21]],"date-time":"2024-06-21T00:00:00Z","timestamp":1718928000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Researchers Supporting Project","award":["RSP2024R296"],"award-info":[{"award-number":["RSP2024R296"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Image semantic segmentation using deep learning algorithms plays a vital role in identifying different rock-forming minerals. In this paper, we employ the U-net model for its architecture that guarantees precise localization and efficient data utilization. We implement this deep learning model across two distinct datasets: (1) the first dataset from the ALEX Streckeisen website, and (2) the second dataset from the Gabal Nikeiba area, South Eastern Desert of Egypt. Our model exhibits excellent performance in both datasets, with an average accuracy of precision at 0.89 and 0.83, recall at 0.80 and 0.78, and F1 score at 0.82 and 0.79, respectively, helping in identifying and detecting rock-forming minerals in thin-section images. The model\u2019s most exceptional performance is clearly in eleven different basement rock-forming minerals with precision up to 0.89, recall at 0.80, and F1 score at 0.82 on average. This study is significant as it represents the key to identifying and detecting minerals in the thin sections of rock samples in Egypt and the Arabian\u2013Nubian Shield as a whole. By significantly reducing analysis time and improving accuracy compared to manual methods, it revolutionizes geological research and resource exploration in the region.<\/jats:p>","DOI":"10.3390\/rs16132276","type":"journal-article","created":{"date-parts":[[2024,6,21]],"date-time":"2024-06-21T11:10:28Z","timestamp":1718968228000},"page":"2276","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Semantic Segmentation of Some Rock-Forming Mineral Thin Sections Using Deep Learning Algorithms: A Case Study from the Nikeiba Area, South Eastern Desert, Egypt"],"prefix":"10.3390","volume":"16","author":[{"given":"Safaa M.","family":"Hassan","sequence":"first","affiliation":[{"name":"National Authority for Remote Sensing and Space Sciences (NARSS), Cairo 1564, Egypt"}]},{"given":"Noureldin","family":"Laban","sequence":"additional","affiliation":[{"name":"National Authority for Remote Sensing and Space Sciences (NARSS), Cairo 1564, Egypt"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5759-487X","authenticated-orcid":false,"given":"Saif M.","family":"Abo Khashaba","sequence":"additional","affiliation":[{"name":"Geology Department, Faculty of Science, Kafrelsheikh University, Kafrelsheikh 33516, Egypt"}]},{"given":"N. H.","family":"El-Shibiny","sequence":"additional","affiliation":[{"name":"Geology Department, Faculty of Science, Kafrelsheikh University, Kafrelsheikh 33516, Egypt"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0384-9061","authenticated-orcid":false,"given":"Bashar","family":"Bashir","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia"}]},{"given":"Mokhles K.","family":"Azer","sequence":"additional","affiliation":[{"name":"Geological Sciences Department, National Research Centre, Cairo 12622, Egypt"}]},{"given":"Kirsten","family":"Dr\u00fcppel","sequence":"additional","affiliation":[{"name":"Karlsruhe Institute of Technology, Institute of Applied Geosciences, Adenauerring 20b, 76131 Karlsruhe, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8043-1227","authenticated-orcid":false,"given":"Hatem M.","family":"Keshk","sequence":"additional","affiliation":[{"name":"National Authority for Remote Sensing and Space Sciences (NARSS), Cairo 1564, Egypt"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.cageo.2015.12.008","article-title":"Estimating elastic moduli of rocks from thin sections: Digital rock study of 3D properties from 2D images","volume":"88","author":"Saxena","year":"2016","journal-title":"Comput. Geosci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.cageo.2017.02.014","article-title":"Estimating permeability from thin sections without reconstruction: Digital rock study of 3D properties from 2D images","volume":"102","author":"Saxena","year":"2017","journal-title":"Comput. Geosci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"104778","DOI":"10.1016\/j.cageo.2021.104778","article-title":"Application of deep learning for semantic segmentation of sandstone thin sections","volume":"152","author":"Saxena","year":"2021","journal-title":"Comput. Geosci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2332","DOI":"10.1007\/s10489-021-02530-z","article-title":"Deep neural networks for automatic grain-matrix segmentation in plane and cross-polarized sandstone photomicrographs","volume":"52","author":"Das","year":"2021","journal-title":"Appl. Intell."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"740638","DOI":"10.3389\/feart.2022.740638","article-title":"Automated segmentation of olivine phenocrysts in a volcanic rock thin section using a fully convolutional neural network","volume":"10","author":"Leichter","year":"2022","journal-title":"Front. Earth Sci."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Nath, F., Asish, S., Sutradhar, S., Li, Z., Shahadat, N., Debi, H.R., and Hoque, S.S. (2023, January 13\u201315). Rock thin-section analysis and mineral detection utilizing deep learning approach. Proceedings of the Unconventional Resources Technology Conference, Denver, CO, USA.","DOI":"10.15530\/urtec-2023-3865660"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ransinangue, A., Labourdette, R., Houzay, E., Chehata, N., Bourillot, R., Guillon, S., and Dujoncquoy, E. (2024, January 25\u201327). Carbonates Thin Section Segmentation based on a Synthetic Data Training Approach. Proceedings of the Fourth EAGE Digitalization Conference & Exhibition, Paris, France. European Association of Geoscientists & Engineers.","DOI":"10.3997\/2214-4609.202439066"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1111\/jmi.13261","article-title":"A multiangle polarised imaging-based method for thin section segmentation","volume":"294","author":"Chen","year":"2024","journal-title":"J. Microsc."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"121997","DOI":"10.1016\/j.chemgeo.2024.121997","article-title":"Tracking element-mineral associations with unsupervised learning and dimensionality reduction in chemical and optical image stacks of thin sections","volume":"650","author":"Zamora","year":"2024","journal-title":"Chem. Geol."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Li, D., Zhao, J., and Ma, J. (2022). Experimental studies on rock thin-section image classification by deep learning-based approaches. Mathematics, 10.","DOI":"10.3390\/math10132317"},{"key":"ref_11","first-page":"69","article-title":"The sandstones of the Upper Cretaceous flysch of the Modenese Apennines: Correlations with the Monghidoro flysch","volume":"12","author":"Gazzi","year":"1966","journal-title":"Mineral. Petrogr. Acta"},{"key":"ref_12","first-page":"695","article-title":"Interpreting detrital modes of graywacke and arkose","volume":"40","author":"Dickinson","year":"1970","journal-title":"J. Sediment. Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.asoc.2018.05.018","article-title":"A survey on deep learning techniques for image and video semantic segmentation","volume":"70","author":"Oprea","year":"2018","journal-title":"Appl. Soft Comput."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Tzepkenlis, A., Marthoglou, K., and Grammalidis, N. (2023). Grammalidis, Efficient deep semantic segmentation for land cover classifica-tion using sentinel imagery. Remote Sens., 15.","DOI":"10.3390\/rs15082027"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Geiger, A., Lenz, P., and Urtasun, R. (2012, January 16\u201321). Are we ready for autonomous driving? The KITTI vision benchmark suite. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA.","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"473","DOI":"10.5194\/isprs-annals-III-3-473-2016","article-title":"Semantic segmentation of aerial images with an ensemble of CNSS","volume":"3","author":"Marmanis","year":"2016","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_17","first-page":"1","article-title":"Comparison of support vector machine and neutral network classification method in hyperspectral mapping of ophiolite m\u00e9langes\u2013A case study of east of Iran","volume":"20","author":"Bahrambeygi","year":"2017","journal-title":"Egypt. J. Remote Sens. Space Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1016\/j.powtec.2019.12.026","article-title":"Pore-throat characterization of unconsolidated porous media using watershed-segmentation algorithm","volume":"362","author":"Patmonoaji","year":"2019","journal-title":"Powder Technol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.cageo.2019.05.009","article-title":"Mineral grains recognition using computer vision and machine learning","volume":"130","author":"Maitre","year":"2019","journal-title":"Comput. Geosci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"104518","DOI":"10.1016\/j.marpetgeo.2020.104518","article-title":"Machine learning for point counting and segmentation of arenite in thin section","volume":"120","author":"Tang","year":"2020","journal-title":"Mar. Pet. Geol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.cageo.2017.03.011","article-title":"The application of artificial intelligence for the identification of the maceral groups and mineral components of coal","volume":"103","author":"Mlynarczuk","year":"2017","journal-title":"Comput. Geosci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"012046","DOI":"10.1088\/1755-1315\/1151\/1\/012046","article-title":"Building YoloV4 models for identification of rock minerals in thin section","volume":"1151","author":"Pratama","year":"2023","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Dell\u2019aversana, P. (2023). An Integrated Deep Learning Framework for Classification of Mineral Thin Sections and Other Geo-Data, a Tutorial. Minerals, 13.","DOI":"10.3390\/min13050584"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1231","DOI":"10.1007\/s40747-023-01208-y","article-title":"The edge segmentation of grains in thin-section petrographic images utilising ex-tinction consistency perception network","volume":"10","author":"Zhang","year":"2024","journal-title":"Complex Intell. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1477","DOI":"10.1007\/s12145-020-00505-1","article-title":"Rock classification in petrographic thin section images based on concatenated con-volutional neural networks","volume":"13","author":"Su","year":"2020","journal-title":"Earth Sci. Inform."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1605","DOI":"10.1016\/j.petsci.2022.03.011","article-title":"Rock thin-section analysis and identification based on artificial intelligent technique","volume":"19","author":"Liu","year":"2022","journal-title":"Pet. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.cageo.2016.10.010","article-title":"An intelligent system for mineral identification in thin sections based on a cascade approach","volume":"99","author":"Izadi","year":"2017","journal-title":"Comput. Geosci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"106382","DOI":"10.1016\/j.petrol.2019.106382","article-title":"Digital petrography: Mineralogy and porosity identification using machine learning algorithms in petrographic thin section images","volume":"183","author":"Rubo","year":"2019","journal-title":"J. Pet. Sci. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Budennyy, S., Pachezhertsev, A., Bukharev, A., Erofeev, A., Mitrushkin, D., and Belozerov, B. (2017, January 16\u201318). Image processing and machine learning approaches for petrographic thin section analysis. Proceedings of the SPE Russian Petroleum Technology Conference, Moscow, Russia.","DOI":"10.2118\/187885-RU"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1007\/s10596-014-9459-2","article-title":"Search of visually similar microscopic rock images","volume":"19","year":"2015","journal-title":"Comput. Geosci."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Abdel Gawad, A.E., Eliwa, H., Ali, K.G., Alsafi, K., Murata, M., Salah, M.S., and Hanfi, M.Y. (2022). Cancer Risk Assessment and Geo-chemical Features of Granitoids at Nikeiba, Southeastern Desert, Egypt. Minerals, 12.","DOI":"10.3390\/min12050621"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"97","DOI":"10.2475\/ajs.285.2.97","article-title":"Geochronologic and isotopic constraints on late Precambrian crustal evolution in the Eastern Desert of Egypt","volume":"285","author":"Stern","year":"1985","journal-title":"Am. J. Sci."},{"key":"ref_33","unstructured":"Abo Khashaba, S.M. (2022). Integration of Remote Sensing and Geochemical Data for the Exploration of Some Rare Metals-Bearing Gra-Nitic Plutons, Central Eastern Desert, Egypt. [Master\u2019s Thesis, Kafrelsheikh University]."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning representations by back-propagating errors","volume":"323","author":"Rumelhart","year":"1986","journal-title":"Nature"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. International Con-ference on Medical Image Computing and Computer Assisted Intervention, Springer.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_36","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 6\u201311). Batch normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of the International Conference on Machine Learning, PMLR, Lille, France."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1109\/LGRS.2018.2802944","article-title":"Road extraction by deep residual u-net","volume":"15","author":"Zhang","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_38","first-page":"1","article-title":"Data balancing method for training segmentation neural networks","volume":"2744","author":"Kochkarev","year":"2020","journal-title":"CEUR Workshop Proc."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Kuhn, M., and Johnson, K. (2013). Applied Predictive Modeling, Springer.","DOI":"10.1007\/978-1-4614-6849-3"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"12805","DOI":"10.1038\/s41598-024-63430-z","article-title":"Research on the generation and annotation method of thin section images of tight oil reservoir based on deep learning","volume":"14","author":"Liu","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"164","DOI":"10.2113\/gsecongeo.53.2.164","article-title":"Alteration of biotite under mesothermal conditions","volume":"53","author":"Schwartz","year":"1958","journal-title":"Econ. Geol."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/13\/2276\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:02:37Z","timestamp":1760108557000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/13\/2276"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,21]]},"references-count":41,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2024,7]]}},"alternative-id":["rs16132276"],"URL":"https:\/\/doi.org\/10.3390\/rs16132276","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,21]]}}}