Paper Info
Title
Spatiotemporal Detection and Analysis of Urban Villages in Mega City Regions of China Using High-Resolution Remotely Sensed Imagery
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.
Year
DOI
Venue
2015
10.1109/TGRS.2014.2380779
IEEE T. Geoscience and Remote Sensing
Keywords
Field
DocType
remote sensing,index-based approach,socioeconomic impact,mega city regions,training samples,uv identification,scene-based method,geographic feature extraction,supervised latent dirichlet allocation,urbanization,image resolution,china,multi-index scene model,spatiotemporal phenomena,spatiotemporal analysis,feature extraction,image classification,geophysical image processing,urban village (uv),multitemporal image classification,object detection,town and country planning,urban villages,natural scenes,socio-economic effects,kappa values,spatiotemporal detection,scene-based classification,settlement,bag-of-visual words,high resolution remotely sensed imagery,vectors,bag of visual words,semantics
Computer vision,Urbanization,Object detection,Latent Dirichlet allocation,Bag-of-words model in computer vision,Remote sensing,Urban planning,Artificial intelligence,Human settlement,Megacity,Contextual image classification,Mathematics
Journal
Volume
Issue
ISSN
53
7
0196-2892
Citations 
PageRank 
References 
14
0.65
12
Authors
3
Name
Order
Citations
PageRank
Xin Huang1130174.47
Hui Liu2553.22
Liangpei Zhang35448307.02