A global land cover training dataset from 1984 to 2020

A global land cover training dataset from 1984 to 2020

Abstract State-of-the-art cloud computing platforms such as Google Earth Engine (GEE) enable regional-to-global land cover and land cover change mapping with machine learning algorithms. However, collection of high-quality training data, which is necessary for accurate land cover mapping, remains co...

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Bibliographic Details
Main Authors: Radost Stanimirova, Katelyn Tarrio, Konrad Turlej, Kristina McAvoy, Sophia Stonebrook, Kai-Ting Hu, Paulo Arévalo, Eric L. Bullock, Yingtong Zhang, Curtis E. Woodcock, Pontus Olofsson, Zhe Zhu, Christopher P. Barber, Carlos M. Souza, Shijuan Chen, Jonathan A. Wang, Foster Mensah, Marco Calderón-Loor, Michalis Hadjikakou, Brett A. Bryan, Jordan Graesser, Dereje L. Beyene, Brian Mutasha, Sylvester Siame, Abel Siampale, Mark A. Friedl
Format: Article
Language:English
Published: Nature Portfolio 2023-12-01
Series:Scientific Data
Online Access:https://doi.org/10.1038/s41597-023-02798-5

Internet

https://doi.org/10.1038/s41597-023-02798-5

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