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 $9,600,000 km^2$, 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.

翻译：当前已有多种大尺度土地覆盖产品发布，但针对中国的现有产品要么缺乏高空间分辨率，要么未实现全国覆盖。随着中国快速城市化进程，亟需创建全国范围的极高分辨率（VHR）土地覆盖图。本研究通过深度学习框架与多源开源数据，首次构建了覆盖960万平方公里的中国1米分辨率土地覆盖图——SinoLC-1。为高效生成全国级VHR土地覆盖图，首先从3套10米分辨率土地覆盖产品与开放街道地图数据中收集可靠标注样本；其次利用收集的10米标签与1米谷歌地球影像，通过本文提出的低分辨率到高分辨率（L2H）框架进行训练。该框架采用弱监督与自监督策略，有效解决了训练数据分辨率不匹配导致的标签噪声问题，并生成VHR结果。最后，我们将SinoLC-1与五种主流产品进行对比，并使用包含106,852个样本点的验证集及政府统计报告进行精度评估。结果表明，SinoLC-1总体精度达74%，Kappa系数为0.65。作为中国首幅1米分辨率全国土地覆盖图，SinoLC-1在保持最优景观细节的同时展现出总体可接受的制图精度。