Stanimirova, Radost; Tarrio, Katelyn; Turlej, Konrad; McAvoy, Kristina; Stonebrook, Sophia; Hu, Kai-Ting; Arévalo, Paulo; Bullock, Eric L.; Zhang, Yingtong; Woodcock, Curtis E.; Olofsson, Pontus; Zhu, Zhe; Barber, Christopher P.; Souza, Casrlos M.; Chen, Shijuan; Wang, Jonathan A.; Mensah, Foster; Calderon-Loor, Marco; Hadjikakou, Michalis; Bryan, Brett A.; Graesser, Jordan; Beyene, Dereje L.; Mutasha, Brian; Siame, Sylvester; Siampale, Abel; Friedl, Mark A. A global land cover training dataset from 1984 to 2020. Nature, 2023. https://www.nature.com/articles/s41597-023-02798-5
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 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. We sampled the spectral-temporal feature space from Landsat imagery to efciently allocate training data across global ecoregions and incorporated publicly available and collaborator-provided datasets to our database. To refect the underlying regional class distribution and post-disturbance landscapes, we strategically augmented the database. We used a machine learning-based cross-validation procedure to remove potentially mis-labeled training units. Our training database is relevant for a wide array of studies such as land cover change, agriculture, forestry, hydrology, urban development, among many others.