高质量的土地利用数据集对于构建高性能的土地利用分类模型至关重要。由于土地利用的复杂性和空间异质性，数据集构建过程效率低下且成本高昂，这一挑战影响了数据集的质量，进而制约了模型性能。新兴的数据中心人工智能（Data-Centric Artificial Intelligence, DCAI）领域有望提供数据集优化技术，为该问题提供有效解决方案。因此，本研究提出一种名为DCAI-CLUD的数据中心框架，用于土地利用数据集的构建。基于该框架，数据标注的准确率和标注速率分别提升5.93%和28.97%；数据集的Gini指数以及非混合土地利用类别样本占比分别提升3.27%和8.52%；土地利用分类模型的整体精度（OA）和Kappa系数显著提高27.87%和58.08%。本研究首次将DCAI引入地理信息与遥感领域，并验证了其有效性。所提出的框架能够有效提升数据集构建的效率与质量，同时优化模型性能。基于该框架，我们构建了中国主要城市多源土地利用数据集CN-MSLU-100K。本研究提出了优化土地利用数据集构建流程的框架，通过筛选与预标注提升了数据标注的质量与效率；通过数据集优化显著增强了土地利用分类模型的性能；先验结果对标注人员存在主观影响；本研究为首次将DCAI应用于土地利用分类的研究。

A high-quality land-use dataset is crucial for constructing a high-performance land-use classification model. Due to the complexity and spatial heterogeneity of land-use, the dataset construction process is inefficient and costly. This challenge affects the quality of datasets, consequently impacting the model’s performance. The emerging field of Data-Centric Artificial Intelligence (DCAI) is expected to deliver techniques for dataset optimization, offering a promising solution to the problem. Therefore, this study proposes a data-centric framework named DCAI-CLUD for the construction of land-use datasets. Based on this framework, the accuracy and rate of data labeling are improved by 5.93 and 28.97%. The <i>Gini</i> index of the dataset and the proportion of samples with non-mixed land-use categories are enhanced by 3.27 and 8.52%. The overall accuracy (OA) and Kappa of the land-use classification model improved significantly by 27.87 and 58.08%. This study is the first to introduce DCAI into the field of geographic information and remote sensing and verify its effectiveness. The proposed framework can effectively improve the construction efficiency and quality of the dataset and synchronously optimize the model performance. Based on the proposed framework, we constructed a multi-source land-use dataset of major cities in China named CN-MSLU-100K. A framework for optimizing the land-use dataset construction process is proposed.Filtering and pre-labeling improved the quality and efficiency of data labeling.The performance of land-use classification model is enhanced by dataset optimization.Preconceived results have a subjective impact on the data labelers.The first study to introduce DCAI for land-use classification is launched. A framework for optimizing the land-use dataset construction process is proposed. Filtering and pre-labeling improved the quality and efficiency of data labeling. The performance of land-use classification model is enhanced by dataset optimization. Preconceived results have a subjective impact on the data labelers. The first study to introduce DCAI for land-use classification is launched.