{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T18:59:37Z","timestamp":1772823577230,"version":"3.50.1"},"reference-count":55,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2019,8,15]],"date-time":"2019-08-15T00:00:00Z","timestamp":1565827200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["17K19965"],"award-info":[{"award-number":["17K19965"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In-time and accurate monitoring of land cover and land use are essential tools for countries to achieve sustainable food production. However, many developing countries are struggling to efficiently monitor land resources due to the lack of financial support and limited access to adequate technology. This study aims at offering a solution to fill in such a gap in developing countries, by developing a land cover solution that is free of costs. A fully automated framework for land cover mapping was developed using 10-m resolution open access satellite images and machine learning (ML) techniques for the African country of Lesotho. Sentinel-2 satellite images were accessed through Google Earth Engine (GEE) for initial processing and feature extraction at a national level. Also, Food and Agriculture Organization\u2019s land cover of Lesotho (FAO LCL) data were used to train a support vector machine (SVM) and bagged trees (BT) classifiers. SVM successfully classified urban and agricultural lands with 62 and 67% accuracy, respectively. Also, BT could classify the two categories with 81 and 65% accuracy, correspondingly. The trained models could provide precise LC maps in minutes or hours. they can also be utilized as a viable solution for developing countries as an alternative to traditional geographic information system (GIS) methods, which are often labor intensive, require acquisition of very high-resolution commercial satellite imagery, time consuming and call for high budgets.<\/jats:p>","DOI":"10.3390\/rs11161907","type":"journal-article","created":{"date-parts":[[2019,8,15]],"date-time":"2019-08-15T11:11:00Z","timestamp":1565867460000},"page":"1907","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Integration of Machine Learning and Open Access Geospatial Data for Land Cover Mapping"],"prefix":"10.3390","volume":"11","author":[{"given":"Mohammad","family":"Mardani","sequence":"first","affiliation":[{"name":"Graduate School of Bio-Applications and Systems Engineering, Tokyo University of Agriculture and Technology, Tokyo 183-8538, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hossein","family":"Mardani","sequence":"additional","affiliation":[{"name":"Department of International Environmental and Agricultural Sciences, Tokyo University of Agriculture and Technology, Tokyo 183-8538, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lorenzo","family":"De Simone","sequence":"additional","affiliation":[{"name":"Climate, Biodiversity, Land and Water Department, Food and Agriculture Organization of the United Nations, 00153 Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Samuel","family":"Varas","sequence":"additional","affiliation":[{"name":"Information and Technology Division (CIO) of Food and Agriculture Organization of the United Nations, 00153 Rome, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Naoki","family":"Kita","sequence":"additional","affiliation":[{"name":"Graduate School of Engineering, Tokyo University of Agriculture and Technology, Tokyo 183-8538, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Takafumi","family":"Saito","sequence":"additional","affiliation":[{"name":"Institute of Engineering, Tokyo University of Agriculture and Technology, Tokyo 183-8538, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,8,15]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"World population prospects: The 2015 revision","volume":"33","author":"Nations","year":"2015","journal-title":"U. 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