# National-scale 1-m resolution land-cover mapping for the entire China based on a low-cost solution and open-access data

arXiv: https://arxiv.org/abs/2303.05305 · HTML: https://mlsanity.com/paper/2303.05305/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 8 references that are in the map.
- First posted on arXiv 9 Mar 2023.

## Builds on (most cited first, 1 of 1)

- [LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation](https://mlsanity.com/paper/2110.08733/) (2021)

## Abstract (arXiv)

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 9,600,000 km2, 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 between training pairs and produces VHR results. Lastly, we compare the SinoLC-1 with five widely used products and validate it with a sample set including 10,6852 points and a statistical report collected from the government. The results show the SinoLC-1 achieved an OA of 74% and a Kappa of 0.65. Moreover, as the first 1-m national-scale LC map for China, the SinoLC-1 shows overall acceptable results with the finest landscape details.