ArticleScientific data2025
A high-resolution global annual city and town boundaries dataset (2000-2022) derived from GLC_FCS30D product.
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 1 paper.
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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
1 citing paper in PubMed.
- A high-resolution global annual city and town boundaries dataset (2000-2022) derived from GLC_FCS30D product.Scientific data · 2025Article
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Urban boundaries are essential indicators for understanding spatial structure and dynamic changes in human settlements. Most existing high-resolution urban boundaries datasets performed poorly on distinguishing low-density built-up areas from non-urban spaces and overlooking the role of population distribution in defining urban extents. In this study, we developed a dual-threshold method by integrating impervious surface density and population data to map annual city and town boundaries from GISD30 (global 30 m impervious-surface dynamic dataset). Specifically, we combined kernel density estimation and cellular automata algorithms to generate global urban boundaries, and then differentiated urban settlement types (e.g., cities and towns) based on population thresholds, thereby producing the Global City and Town Boundaries (GCTB) dataset at 30 m resolution for the period 2000-2022. The GCTB dataset achieves strong agreement with the high-resolution urban boundary interpretation dataset-Atlas of Urban Expansion (R² > 0.88). Using OpenStreetMap place tags, the City/Town split reaches precision 0.80 (City) and 0.65 (Town) with overall accuracy 0.75. Therefore, GCTB provides essential spatial information for global urbanization and sustainable-development planning.
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