ArticleScientific data2025
MSLU-100K: A Large Multi-Source Dataset for Land Use Analysis in Major Chinese Cities.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Urban land use of national economic sectors in Guangdong-Hong Kong-Macao Greater Bay Area from 2015-2022.Scientific data · 2026Article
- A multi-label dataset for China's agricultural and rural scenes classification from VHR satellite imagery.Scientific data · 2026Article
- MSLU-100K: A Large Multi-Source Dataset for Land Use Analysis in Major Chinese Cities.Scientific data · 2025Article
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Authors and funding
5 authors.
Funding
Abstract
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.
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Registered trials
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