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Recent advancements in satellite data accessibility, computing power, and satellite technologies have boosted large-extent high-resolution land cover mapping. However, retrieving a sufficient amount of reliable training data for the production of such land cover maps is typically a demanding task, especially using modern deep learning classification techniques that require larger training sample sizes compared to traditional machine learning methods. In view of the above, this study developed a new benchmark dataset called the Map of Land Cover Agreement (MOLCA). MOLCA was created by integrating multiple existing high-resolution land cover datasets through a consensus-based approach. Covering Sub-Saharan Africa, the Amazon, and Siberia, this dataset encompasses approximately 117 billion 10m pixels across three macro-regions. The MOLCA legend aligns with most of the global high-resolution datasets and consists of nine distinct land cover classes. Noteworthy advantages of MOLCA include a higher number of pixels as well as coverage for typically underrepresented regions in terms of training data availability. With an estimated overall accuracy of 96%, MOLCA holds great potential as a valuable resource for the production of future high-resolution land cover maps.<\/jats:p>","DOI":"10.3390\/rs15153774","type":"journal-article","created":{"date-parts":[[2023,7,31]],"date-time":"2023-07-31T01:48:50Z","timestamp":1690768130000},"page":"3774","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Map of Land Cover Agreement: Ensambling Existing Datasets for Large-Scale Training Data Provision"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2777-3751","authenticated-orcid":false,"given":"Gorica","family":"Bratic","sequence":"first","affiliation":[{"name":"Department of Civil and Environmental Engineering, Politecnico di Milano, 20133 Milan, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3226-5586","authenticated-orcid":false,"given":"Daniele","family":"Oxoli","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, Politecnico di Milano, 20133 Milan, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3161-5561","authenticated-orcid":false,"given":"Maria Antonia","family":"Brovelli","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, Politecnico di Milano, 20133 Milan, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,29]]},"reference":[{"key":"ref_1","unstructured":"Bontemps, S., Defourny, P., Radoux, J., Van Bogaert, E., Lamarche, C., Achard, F., Mayaux, P., Boettcher, M., Brockmann, C., and Kirches, G. 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