SinoLC-1: the first 1 m resolution national-scale land-cover map of China created with a deep learning framework and open-access data

Abstract. 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. Constructing a very-high-resolution (VHR) land-cover dataset for China with national coverage, however, is a nontrivial task. Thus, this has become an active area of research that is impeded by the challenges of image acquisition, manual annotation, and computational complexity. To fill this gap, the first 1 m 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, OpenStreetMap (OSM), and Google Earth imagery. Reliable training…

• NN
  National Natural Science Foundation of China

  Award: 42071322
• NS
  Natural Science Foundation of Hubei Province

  Award: 2020CFA053
• NK
  National Key Research and Development Program of China

  Award: 2022YFB3903605