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Fully Convolutional Neural Networks for Large Scale Cropland Mapping with Historical Label Dataset

Cropland is one of highly variable land cover on earth surface, which is greatly significant for grain production and food security. In this work we developed a strategy of temporal transfer with fully convolutional neural networks for cropland mapping using high resolution remote sensing imagery. The results showed cropland mapping with historical label dataset performed well with 90.05% overall accuracy, while the accuracy slightly increased by 1.15% after fine-tuning with a small new dataset. This means it is a practical proposal to monitor cropland efficiently and accurately using historical sample dataset.