A spatiotemporal ensemble machine learning framework for generating land use/land cover time-series maps for Europe (2000–2019) based on LUCAS, CORINE and GLAD Landsat - Witjes, Martijn; Parente, Leandro; van Diemen, Chris J.; Hengl, Tomislav; Landa, Martin; Brodský, Lukáš; Halounova, Lena; Križan, Josip; Antonić, Luka; Ilie, Codrina Maria; Craciunescu, Vasile; Kilibarda, Milan; et al. (PeerJ (San Francisco, CA), 2022)
Keywords: Accuracy; Automation; Classification; Climate change; Data Mining and Machine Learning; Data Science; Earth resources technology satellites; Ensemble; Environmental Monitoring; Europe; Land use; Landsat; Learning algorithms; Machine learning; Neural networks; Probability; Remote sensing; Spatial analysis; Spatial and Geographic Information Science; Spatiotemporal; Surface water; Time Factors; Urbanization; Europe; United Kingdom; Sweden; Uncertainty; Land use/land cover; Ensemble; Landsat; Machine learning; Probability; Spatial analysis; Big data; Spatiotemporal; Environmental monitoring