Abstract
National land cover map with 30m resolution, an important database for studying the interaction between human and the environment, is a tedious work. The rise of deep learning technique provides a new idea for the work. This paper reports a novel method based on deep convolutional neural networks for the national land cover mapping task. The proposed method has four major parts: classification system, data sources, training samples selection, and training & inferencing. The produced deep learning (DL)-based land cover map is compared with two highly accepted land cover maps (the reference land cover map and the GLC30). Overall accuracies of GLC30 and DL-land cover map are 76.45% and 82.59% when considering the reference land cover map as the ground truth. Overall accuracies of the reference land cover map and the DL-land cover map are 74.25% and 78.87% when the GLC30 is treated as the ground truth.
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