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A high-resolution nationwide urban village mapping product for 342 Chinese cities based on foundation models

arXiv 2602.18765 ↗First posted 21 Feb 2026cs.CVcited by 0

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Abstract (arXiv)

Urban Villages (UVs) represent a distinctive form of high-density informal settlement embedded within China's rapidly urbanizing cities. Accurate identification of UVs is critical for urban governance, renewal, and sustainable development. But due to the pronounced heterogeneity and diversity of UVs across China's vast territory, a consistent and reliable nationwide dataset has been lacking. In this work, we present GeoLink-UV, a high-resolution nationwide UV mapping product that clearly delineates the locations and boundaries of UVs in 342 Chinese cities. The dataset is derived from multisource geospatial data, including optical remote sensing images and geo-vector data, and is generated through a foundation model-driven mapping framework designed to address the generalization issues and improve the product quality. A geographically stratified accuracy assessment based on independent samples from 28 cities confirms the reliability and scientific credibility of the nationwide dataset across heterogeneous urban contexts. Based on this nationwide product, we reveal substantial interregional disparities in UV prevalence and spatial configuration. On average, UV areas account for 8 % of built-up land, with marked clustering in central and south China. Building-level analysis further confirms a consistent low-rise, high-density development pattern of UVs nationwide, while highlighting regionally differentiated morphological characteristics. The GeoLink-UV dataset provides an open and systematically validated geospatial foundation for urban studies, informal settlement monitoring, and evidence-based urban renewal planning, and contributes directly to large-scale assessments aligned with Sustainable Development Goal 11. The GeoLink-UV dataset introduced in this article is freely available at https://doi.org/10.5281/zenodo.18688062.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 29 references that are in the map. First posted on arXiv 21 Feb 2026.

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References · 9

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  6. 2024Prithvi-EO-2.0: A Versatile Multi-Temporal Foundation Model for Earth Observation Applicationscited by 139
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  8. 2025GeoLink: Empowering Remote Sensing Foundation Model with OpenStreetMap Datacited by 7
  9. 2025Generalizable Slum Detection from Satellite Imagery with Mixture-of-Expertscited by 6

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