{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T12:52:47Z","timestamp":1780318367863,"version":"3.54.1"},"reference-count":131,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,10,31]],"date-time":"2022-10-31T00:00:00Z","timestamp":1667174400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), University of Technology Sydney","award":["RSP-2021\/14"],"award-info":[{"award-number":["RSP-2021\/14"]}]},{"name":"Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), University of Technology Sydney","award":["UKM-YSD-2021-003"],"award-info":[{"award-number":["UKM-YSD-2021-003"]}]},{"DOI":"10.13039\/501100002383","name":"Researchers Supporting Project, King Saud University, Riyadh, Saudi Arabia","doi-asserted-by":"publisher","award":["RSP-2021\/14"],"award-info":[{"award-number":["RSP-2021\/14"]}],"id":[{"id":"10.13039\/501100002383","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002383","name":"Researchers Supporting Project, King Saud University, Riyadh, Saudi Arabia","doi-asserted-by":"publisher","award":["UKM-YSD-2021-003"],"award-info":[{"award-number":["UKM-YSD-2021-003"]}],"id":[{"id":"10.13039\/501100002383","id-type":"DOI","asserted-by":"publisher"}]},{"name":"UKM YSDChair of Sustainability","award":["RSP-2021\/14"],"award-info":[{"award-number":["RSP-2021\/14"]}]},{"name":"UKM YSDChair of Sustainability","award":["UKM-YSD-2021-003"],"award-info":[{"award-number":["UKM-YSD-2021-003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In this era of free and open-access satellite and spatial data, modern innovations in cloud computing and machine-learning algorithms (MLAs) are transforming how Earth-observation (EO) datasets are utilized for geological mapping. This study aims to exploit the potentialities of the Google Earth Engine (GEE) cloud platform using powerful MLAs. The proposed method is implemented in three steps: (1) Based on GEE and Sentinel 2A imagery (spectral and textural features), that cover 1283 km2 area, a variety of lithological maps are generated using five supervised classifiers (random forest (RF), support vector machine (SVM), classification and regression tree (CART), minimum distance (MD), na\u00efve Bayes (NB)); (2) the accuracy assessments for each class are performed, by estimating overall accuracy (OA) and kappa coefficient (K) for each classifier; (3) finally, the fusion of classification maps is performed using Dempster\u2013Shafer Theory (DST) for mapping lithological units of the northern part of the complex Paleozoic massif of Rehamna, a large semi-arid region located in the SW of the western Moroccan Meseta. The results were quantitatively compared with existing geological maps, enhanced color composite and validated by field survey investigation. In comparison of individual classifiers, the SVM yields better accuracy of nearly 88%, which was 12% higher than the RF MLA; otherwise, the parametric MLAs produce the weakest lithological maps among other classifiers, with a lower OA of approximately 67%, 54% and 52% for CART, MD and NB, respectively. Noticeably, the highest OA value of 96% is achieved for the proposed approach. Therefore, we conclude that this method allows geoscientists to update previous geological maps and rapidly produce more precise lithological maps, especially for hard-to-reach regions.<\/jats:p>","DOI":"10.3390\/rs14215498","type":"journal-article","created":{"date-parts":[[2022,11,2]],"date-time":"2022-11-02T06:49:02Z","timestamp":1667371742000},"page":"5498","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Improved Lithological Map of Large Complex Semi-Arid Regions Using Spectral and Textural Datasets within Google Earth Engine and Fused Machine Learning Multi-Classifiers"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8134-7537","authenticated-orcid":false,"given":"Imane","family":"Serbouti","sequence":"first","affiliation":[{"name":"Laboratory of Applied Geology, Geomatic and Environment, Department of Geology, Faculty of Sciences Ben M\u2019Sik, Hassan II University of Casablanca, Casablanca 20000, Morocco"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammed","family":"Raji","sequence":"additional","affiliation":[{"name":"Laboratory of Applied Geology, Geomatic and Environment, Department of Geology, Faculty of Sciences Ben M\u2019Sik, Hassan II University of Casablanca, Casablanca 20000, Morocco"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mustapha","family":"Hakdaoui","sequence":"additional","affiliation":[{"name":"Laboratory of Applied Geology, Geomatic and Environment, Department of Geology, Faculty of Sciences Ben M\u2019Sik, Hassan II University of Casablanca, Casablanca 20000, Morocco"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fouad","family":"El Kamel","sequence":"additional","affiliation":[{"name":"Laboratory of Geosciences Applied to Urban Development Engineering (GAIA), Department of Geology, University Hassan II-Faculty of Sciences, Casablanca 20000, Morocco"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9863-2054","authenticated-orcid":false,"given":"Biswajeet","family":"Pradhan","sequence":"additional","affiliation":[{"name":"Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), School of Civil and Environmental Engineering, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia"},{"name":"Earth Observation Centre, Institute of Climate Change, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shilpa","family":"Gite","sequence":"additional","affiliation":[{"name":"Artificial Intelligence and Machine Learning Department, Symbiosis Institute of Technology, Sym-Biosis International (Deemed) University, Pune 412115, India"},{"name":"Symbiosis Centre of Applied AI (SCAAI), Symbiosis International (Deemed) University, Pune 412115, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdullah","family":"Alamri","sequence":"additional","affiliation":[{"name":"Department of Geology and Geophysics, College of Science, King Saud University, Riyadh 11451, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9215-2778","authenticated-orcid":false,"given":"Khairul Nizam Abdul","family":"Maulud","sequence":"additional","affiliation":[{"name":"Earth Observation Centre, Institute of Climate Change, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia"},{"name":"Department of Civil Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2876-4080","authenticated-orcid":false,"given":"Abhirup","family":"Dikshit","sequence":"additional","affiliation":[{"name":"Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), School of Civil and Environmental Engineering, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ye, B., Tian, S., Ge, J., and Sun, Y. 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