These training labels and 1-meter resolution images derived from Google Earth were used to train the proposed framework. Reliable training labels were generated by combining three 10-meter GLC products and OSM data. To fill this gap, the first 1-meter resolution national-scale land-cover map of China, SinoLC-1, was established using a deep learning-based framework and open-access data including global land-cover (GLC) products, open street map (OSM), and Google Earth imagery. ![]() Constructing a very-high-resolution (VHR) land-cover dataset for China with national coverage, however, is a non-trivial task and thus, an active area of research impeded by the challenges of image acquisition, manual annotation, and computational complexity. In China, the demand for a more precise perception of the national land surface has become most urgent given the pace of development and urbanization.
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