Ontology highlight
ABSTRACT:
SUBMITTER: Stanimirova R
PROVIDER: S-EPMC10703991 | biostudies-literature | 2023 Dec
REPOSITORIES: biostudies-literature
Stanimirova Radost R Tarrio Katelyn K Turlej Konrad K McAvoy Kristina K Stonebrook Sophia S Hu Kai-Ting KT Arévalo Paulo P Bullock Eric L EL Zhang Yingtong Y Woodcock Curtis E CE Olofsson Pontus P Zhu Zhe Z Barber Christopher P CP Souza Carlos M CM Chen Shijuan S Wang Jonathan A JA Mensah Foster F Calderón-Loor Marco M Hadjikakou Michalis M Bryan Brett A BA Graesser Jordan J Beyene Dereje L DL Mutasha Brian B Siame Sylvester S Siampale Abel A Friedl Mark A MA
Scientific data 20231207 1
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. Ou ...[more]