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
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 costly and labor-intensive. To address this need, we created a global database of nearly 2 million training units spanning the period from 1984 to 2020 for seven primary and nine secondary land cover classes. Our training data collection approach leveraged GEE and machine learning algorithms to ensure data quality and biogeographic representation...
December 7, 2023: Scientific Data