MSLU-100K: A Large Multi-Source Dataset for Land Use Analysis in Major Chinese Cities

High-quality land use datasets are essential for advancing research in land use classification and recognition. However, the complexity and spatial heterogeneity of land use create challenges in dataset construction. To address these issues, we present MSLU-100K, a multi-source land use dataset encompassing over 100,000 irregular parcel samples from 81 Chinese cities. Constructed using a human-computer collaboration framework, this dataset integrates remote sensing and POI (Point of Interest) data, categorizing parcels into 7 primary and 28 secondary land use types. A novel multi-level classification approach combines manual labeling and deep learning, ensuring high data quality across six quality levels. Over 57% of the dataset comprises high-quality samples (Levels 4 and 5), which significantly enhance classification performance. The dataset provides a robust resource for land use recognition, urban planning, and spatial research.