A Bayesian-inspired, deep learning, semi-supervised domain adaptation technique for land cover mapping

@article{Lucas2020ABD,
  title={A Bayesian-inspired, deep learning, semi-supervised domain adaptation technique for land cover mapping},
  author={Benjamin Lucas and Charlotte Pelletier and Daniel F. Schmidt and Geoffrey I. Webb and Franccois Petitjean},
  journal={ArXiv},
  year={2020},
  volume={abs/2005.11930}
}
Land cover maps are a vital input variable to many types of environmental research and management. While they can be produced automatically by machine learning techniques, these techniques require substantial training data to achieve high levels of accuracy, which are not always available. One technique researchers use when labelled training data are scarce is domain adaptation (DA) -- where data from an alternate region, known as the source domain, are used to train a classifier and this model… Expand
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