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• W2024106504 abstract "Due to the rapid urbanization of China, many villages in the urban fringe are enveloped by ever-expanding cities and become so-called urban villages (UVs) with substandard living conditions. Despite physical similarities to informal settlements in other countries (e.g., slums in India), UVs have access to basic public services, and more importantly, villagers own the land legitimately. The resulting socio-economic impact on urban development attracts increasing interest. However, the identification of UVs in previous studies relies on fieldwork, leading to late and incomplete analyses. In this paper, we present three scene-based methods for detecting UVs using high-resolution remotely sensed imagery based on a novel multi-index scene model and two popular scene models, i.e., bag-of-visual-words and supervised latent Dirichlet allocation. In the experiments, our index-based approach produced Kappa values around 0.82 and outperformed conventional models both quantitatively and visually. Moreover, we performed multitemporal classification to evaluate the transferability of training samples across multitemporal images with respect to three methods, and the index-based approach yielded best results again. Finally, using the detection results, we conducted a systematic spatiotemporal analysis of UVs in Shenzhen and Wuhan, two mega cities of China. At the city level, we observe the decline of UVs in urban areas over the recent years. At the block level, we characterize UVs quantitatively from physical and geometrical perspectives and investigate the relationships between UVs and other geographic features. In both levels, the comparison between UVs in Shenzhen and Wuhan is made, and the variations within and across cities are revealed." @default.
• W2024106504 created "2016-06-24" @default.
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• W2024106504 date "2015-07-01" @default.
• W2024106504 modified "2023-10-16" @default.
• W2024106504 title "Spatiotemporal Detection and Analysis of Urban Villages in Mega City Regions of China Using High-Resolution Remotely Sensed Imagery" @default.
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• W2024106504 doi "https://doi.org/10.1109/tgrs.2014.2380779" @default.
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