{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T03:14:55Z","timestamp":1774494895869,"version":"3.50.1"},"reference-count":75,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,2,9]],"date-time":"2022-02-09T00:00:00Z","timestamp":1644364800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Lithological mapping is a critical aspect of geological mapping that can be useful in studying the mineralization potential of a region and has implications for mineral prospectivity mapping. This is a challenging task if performed manually, particularly in highly remote areas that require a large number of participants and resources. The combination of machine learning (ML) methods and remote sensing data can provide a quick, low-cost, and accurate approach for mapping lithological units. This study used deep learning via convolutional neural networks and conventional ML methods involving support vector machines and multilayer perceptron to map lithological units of a mineral-rich area in the southeast of Iran. Moreover, we used and compared the efficiency of three different types of multispectral remote-sensing data, including Landsat 8 operational land imager (OLI), advanced spaceborne thermal emission and reflection radiometer (ASTER), and Sentinel-2. The results show that CNNs and conventional ML methods effectively use the respective remote-sensing data in generating an accurate lithological map of the study area. However, the combination of CNNs and ASTER data provides the best performance and the highest accuracy and adaptability with field observations and laboratory analysis results so that almost all the test data are predicted correctly. The framework proposed in this study can be helpful for exploration geologists to create accurate lithological maps in other regions by using various remote-sensing data at a low cost.<\/jats:p>","DOI":"10.3390\/rs14040819","type":"journal-article","created":{"date-parts":[[2022,2,9]],"date-time":"2022-02-09T21:26:48Z","timestamp":1644442008000},"page":"819","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":91,"title":["A Comparative Study of Convolutional Neural Networks and Conventional Machine Learning Models for Lithological Mapping Using Remote Sensing Data"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4815-0564","authenticated-orcid":false,"given":"Hojat","family":"Shirmard","sequence":"first","affiliation":[{"name":"School of Mining Engineering, College of Engineering, University of Tehran, Tehran P.O. Box 11155-4563, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2618-2458","authenticated-orcid":false,"given":"Ehsan","family":"Farahbakhsh","sequence":"additional","affiliation":[{"name":"EarthByte Group, School of Geosciences, University of Sydney, Sydney, NSW 2006, Australia"}]},{"given":"Elnaz","family":"Heidari","sequence":"additional","affiliation":[{"name":"Department of Mining Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran P.O. Box 15875-4413, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8783-5120","authenticated-orcid":false,"given":"Amin","family":"Beiranvand Pour","sequence":"additional","affiliation":[{"name":"Institute of Oceanography and Environment (INOS), Universiti Malaysia Terengganu (UMT), Kuala Nerus 21030, Terengganu, Malaysia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9863-2054","authenticated-orcid":false,"given":"Biswajeet","family":"Pradhan","sequence":"additional","affiliation":[{"name":"The Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), School of Civil and Environmental Engineering, Faculty of Engineering and IT, University of Technology Sydney, Sydney, NSW 2007, Australia"},{"name":"Center of Excellence for Climate Change Research, King Abdulaziz University, P.O. Box 80234, Jeddah 21589, Saudi Arabia"},{"name":"Earth Observation Centre, Institute of Climate Change, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3334-5764","authenticated-orcid":false,"given":"Dietmar","family":"M\u00fcller","sequence":"additional","affiliation":[{"name":"EarthByte Group, School of Geosciences, University of Sydney, Sydney, NSW 2006, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6353-1464","authenticated-orcid":false,"given":"Rohitash","family":"Chandra","sequence":"additional","affiliation":[{"name":"UNSW Data Science Hub & School of Mathematics and Statistics, University of New South Wales, Sydney, NSW 2052, Australia"},{"name":"Data Analytics for Resources and Environments, Australian Research Council\u2014Industrial Transformation Training Centre,  Canberra, NSW 2052, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6867","DOI":"10.3390\/rs6086867","article-title":"Improving lithological mapping by SVM classification of spectral and morphological features: The discovery of a new chromite body in the Mawat ophiolite complex (Kurdistan, NE Iraq)","volume":"6","author":"Othman","year":"2014","journal-title":"Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Qing, F., Zhao, Y., Meng, X., Su, X., Qi, T., and Yue, D. 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