Predict Land Covers with Transition Modeling and Incremental Learning

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Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data (2016, methodology): “mapping urbanization [68] and tree plantation conversions [69], [70], where hidden Markov models were used to incorporate domain knowledge about the transitions among land”
Machine Learning for the Geosciences: Challenges and Opportunities (2017, methodology): “using spatial as well as temporal properties of the dynamics of plantation conversions [94], [95], [96].”
Spatio-Temporal Data Mining (2017, background): “Variants of recurrent neural networks that include spatial features for spatio-temporal prediction have been explored in [Jain et al. 2016; Jia et al. 2017a,b].”
Machine Learning for the Developing World (2018, background): “Other papers have also explored the use of satellite data to automatically discover land use and land cover (Jia et al. 2017) and agricultural land availability (Chakraborty et al. 2016).”