Mapping the Vanishing and Transformation of Urban Villages in China
- URL: http://arxiv.org/abs/2511.13507v1
- Date: Mon, 17 Nov 2025 15:42:41 GMT
- Title: Mapping the Vanishing and Transformation of Urban Villages in China
- Authors: Wenyu Zhang, Yao Tong, Yiqiu Liu, Rui Cao,
- Abstract summary: Urban settlements embedded within China's urban fabric have undergone widespread demolition and redevelopment in recent decades.<n>There remains a lack of systematic evaluation of whether the demolished land has been effectively reused.<n>This study proposes a deep learning-based framework to monitor changes of UV transformations in China.
- Score: 8.051632857943844
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Urban villages (UVs), informal settlements embedded within China's urban fabric, have undergone widespread demolition and redevelopment in recent decades. However, there remains a lack of systematic evaluation of whether the demolished land has been effectively reused, raising concerns about the efficacy and sustainability of current redevelopment practices. To address the gap, this study proposes a deep learning-based framework to monitor the spatiotemporal changes of UVs in China. Specifically, semantic segmentation of multi-temporal remote sensing imagery is first used to map evolving UV boundaries, and then post-demolition land use is classified into six categories based on the "remained-demolished-redeveloped" phase: incomplete demolition, vacant land, construction sites, buildings, green spaces, and others. Four representative cities from China's four economic regions were selected as the study areas, i.e., Guangzhou (East), Zhengzhou (Central), Xi'an (West), and Harbin (Northeast). The results indicate: 1) UV redevelopment processes were frequently prolonged; 2) redevelopment transitions primarily occurred in peripheral areas, whereas urban cores remained relatively stable; and 3) three spatiotemporal transformation pathways, i.e., synchronized redevelopment, delayed redevelopment, and gradual optimization, were revealed. This study highlights the fragmented, complex and nonlinear nature of UV redevelopment, underscoring the need for tiered and context-sensitive planning strategies. By linking spatial dynamics with the context of redevelopment policies, the findings offer valuable empirical insights that support more inclusive, efficient, and sustainable urban renewal, while also contributing to a broader global understanding of informal settlement transformations.
Related papers
- A high-resolution nationwide urban village mapping product for 342 Chinese cities based on foundation models [5.057687732929524]
GeoLink-UV is a high-resolution nationwide UV mapping product that clearly delineates the locations and boundaries of UVs in 342 Chinese cities.<n>On average, UV areas account for 8 % of built-up land, with marked clustering in central and south China.
arXiv Detail & Related papers (2026-02-21T09:07:23Z) - MMUEChange: A Generalized LLM Agent Framework for Intelligent Multi-Modal Urban Environment Change Analysis [7.396133065771444]
MMUEChange is a multi-modal agent framework that flexibly integrates heterogeneous urban data.<n>Case studies include a shift toward small, community-focused parks in New York, and the spread of concentrated water pollution across districts in Hong Kong.
arXiv Detail & Related papers (2026-01-09T02:34:35Z) - AtlasGS: Atlanta-world Guided Surface Reconstruction with Implicit Structured Gaussians [57.95719610327081]
We propose an Atlanta-world guided implicit-structured Gaussian Splatting that achieves smooth indoor and urban scene reconstruction.<n>Our method outperforms state-of-the-art approaches in both indoor and urban scenes, delivering superior surface reconstruction quality.
arXiv Detail & Related papers (2025-10-29T03:17:58Z) - Urban-R1: Reinforced MLLMs Mitigate Geospatial Biases for Urban General Intelligence [64.36291202666212]
Urban General Intelligence (UGI) refers to AI systems that can understand and reason about complex urban environments.<n>Recent studies have built urban foundation models using supervised fine-tuning (SFT) of LLMs and MLLMs.<n>We propose Urban-R1, a reinforcement learning-based post-training framework that aligns MLLMs with the objectives of UGI.
arXiv Detail & Related papers (2025-10-18T15:59:09Z) - From Drone Imagery to Livability Mapping: AI-powered Environment Perception in Rural China [9.034240130900802]
A Vision-Language Contrastive Ranking Framework (VLCR) is designed for rural livability assessment in China.<n>The framework employs chain-of-thought prompting strategies to guide multimodal large language models (MLLMs) in identifying visual features related to quality of life and ecological habitability from drone photographs.<n>The proposed framework superior performance with a Spearman Footrule distance of 0.74, outperforming mainstream commercial MLLMs by approximately 0.1.
arXiv Detail & Related papers (2025-08-29T16:04:06Z) - UV-SAM: Adapting Segment Anything Model for Urban Village Identification [25.286722125746902]
Governments heavily depend on field survey methods to monitor the urban villages.
To accurately identify urban village boundaries from satellite images, we adapt the Segment Anything Model (SAM) to urban village segmentation, named UV-SAM.
UV-SAM first leverages a small-sized semantic segmentation model to produce mixed prompts for urban villages, including mask, bounding box, and image representations, which are then fed into SAM for fine-grained boundary identification.
arXiv Detail & Related papers (2024-01-16T03:21:42Z) - A Contextual Master-Slave Framework on Urban Region Graph for Urban
Village Detection [68.84486900183853]
We build an urban region graph (URG) to model the urban area in a hierarchically structured way.
Then, we design a novel contextual master-slave framework to effectively detect the urban village from the URG.
The proposed framework can learn to balance the generality and specificity for UV detection in an urban area.
arXiv Detail & Related papers (2022-11-26T18:17:39Z) - Multi-scale Digital Twin: Developing a fast and physics-informed
surrogate model for groundwater contamination with uncertain climate models [53.44486283038738]
Climate change exacerbates the long-term soil management problem of groundwater contamination.
We develop a physics-informed machine learning surrogate model using U-Net enhanced Fourier Neural Contaminated (PDENO)
In parallel, we develop a convolutional autoencoder combined with climate data to reduce the dimensionality of climatic region similarities across the United States.
arXiv Detail & Related papers (2022-11-20T06:46:35Z) - Spatial-Temporal Hypergraph Self-Supervised Learning for Crime
Prediction [60.508960752148454]
This work proposes a Spatial-Temporal Hypergraph Self-Supervised Learning framework to tackle the label scarcity issue in crime prediction.
We propose the cross-region hypergraph structure learning to encode region-wise crime dependency under the entire urban space.
We also design the dual-stage self-supervised learning paradigm, to not only jointly capture local- and global-level spatial-temporal crime patterns, but also supplement the sparse crime representation by augmenting region self-discrimination.
arXiv Detail & Related papers (2022-04-18T23:46:01Z) - COVID-19 is linked to changes in the time-space dimension of human
mobility [0.2544539499281092]
During coronavirus disease 2019 pandemic, mobility patterns were reshaped.
During lockdowns restrictions, the decrease of spatial mobility is interwoven with the emergence of asynchronous mobility dynamics.
In rural and low-income areas, the spatial mobility dimension suffered a more considerable disruption when compared with urbanized and high-income areas.
arXiv Detail & Related papers (2022-01-17T17:06:59Z) - Methodological Foundation of a Numerical Taxonomy of Urban Form [62.997667081978825]
We present a method for numerical taxonomy of urban form derived from biological systematics.
We derive homogeneous urban tissue types and, by determining overall morphological similarity between them, generate a hierarchical classification of urban form.
After framing and presenting the method, we test it on two cities - Prague and Amsterdam.
arXiv Detail & Related papers (2021-04-30T12:47:52Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.