Deep Learning Model Transfer in Forest Mapping using Multi-source Satellite SAR and Optical Images. (arXiv:2308.05005v1 [eess.SP])

Deep learning (DL) models are gaining popularity in forest variable
prediction using Earth Observation images. However, in practical forest
inventories, reference datasets are often represented by plot- or stand-level
measurements, while high-quality representative wall-to-wall reference data for
end-to-end training of DL models are rarely available. Transfer learning
facilitates expansion of the use of deep learning models into areas with
sub-optimal training data by allowing pretraining of the model in areas where
high-quality teaching data are available. In this study, we …

arxiv data datasets deep learning earth earth observation images mapping observation plot practical prediction quality reference satellite training transfer