National-scale 1-m resolution land-cover mapping for the entire China based on a low-cost solution and open-access data
Zhuohong Li, Wei He, Hongyan Zhang

TL;DR
This paper presents SinoLC-1, the first 1-meter resolution land-cover map for China, created using a deep learning framework and open-access data, providing detailed nationwide land-cover information for urbanization and environmental monitoring.
Contribution
The study introduces a novel low-to-high framework that produces a high-resolution national land-cover map using weak and self-supervised learning with open data sources.
Findings
Achieved an overall accuracy of 74% and Kappa of 0.65.
Produced the first 1-m resolution land-cover map for China.
Demonstrated the effectiveness of weakly supervised deep learning for large-scale mapping.
Abstract
Nowadays, many large-scale land-cover (LC) products have been released, however, current LC products for China either lack a fine resolution or nationwide coverage. With the rapid urbanization of China, there is an urgent need for creating a very-high-resolution (VHR) national-scale LC map for China. In this study, a novel 1-m resolution LC map of China covering , called SinoLC-1, was produced by using a deep learning framework and multi-source open-access data. To efficiently generate the VHR national-scale LC map, firstly, the reliable LC labels were collected from three 10-m LC products and Open Street Map data. Secondly, the collected 10-m labels and 1-m Google Earth imagery were utilized in the proposed low-to-high (L2H) framework for training. With weak and self-supervised strategies, the L2H framework resolves the label noise brought by the mismatched resolution…
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Taxonomy
TopicsLand Use and Ecosystem Services · Remote Sensing in Agriculture · Remote Sensing and Land Use
