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Remote Sens. 2017, 9(4), 365; doi:10.3390/rs9040365

The Use of Landscape Metrics and Transfer Learning to Explore Urban Villages in China

1
,
1,2,* , 1,* and 2
1
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
2
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
*
Authors to whom correspondence should be addressed.
Academic Editors: Richard Sliuzas, Xiaofeng Li and Prasad S. Thenkabail
Received: 4 January 2017 / Revised: 18 March 2017 / Accepted: 9 April 2017 / Published: 13 April 2017
(This article belongs to the Special Issue Earth Observation in Planning for Sustainable Urban Development)
View Full-Text   |   Download PDF [3374 KB, uploaded 13 April 2017]   |  

Abstract

Urban villages (UVs), the main settlements of rural migrant workers and low-income groups in metropolitan areas of China, have become of major concern to city managers and researchers due to the rapid urbanization in recent years. A clear understanding of their evolution and spatial relationships with the city is of great importance to policy formulation, implementation and assessment. In this paper, we propose a new framework based on landscape metrics and transfer learning for the long-term monitoring and analysis of UVs, and we apply it to Shenzhen and Wuhan, two metropolitan cities of China, with high-resolution satellite images acquired from 2003–2012 and 2009–2015, respectively. In the framework, landscape metrics are used for identifying the UVs and quantifying their evolution patterns on the basis of a city-UV-building hierarchical landscape model. Transfer learning is also introduced to use the samples and features across the spatial and temporal domains, which reduces the time and labor cost, as well as improves the mapping accuracies by 3–10%. The results show that the total area of UVs has decreased by less than 6 % in Shenzhen and more than 45 % in Wuhan. Moreover, we observe significant spatial correlations in the development of UVs in Shenzhen. By contrast, no strong spatial correlations are found in Wuhan’s UVs, indicating that their development is largely independent of the spatial location. The results reveal two typical strategies, i.e., demolition and renovation, towards the redevelopment of UVs in China. View Full-Text
Keywords: urban village; informal settlement; urbanization; China; landscape metrics; transfer learning; spatial statistics urban village; informal settlement; urbanization; China; landscape metrics; transfer learning; spatial statistics
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Liu, H.; Huang, X.; Wen, D.; Li, J. The Use of Landscape Metrics and Transfer Learning to Explore Urban Villages in China. Remote Sens. 2017, 9, 365.

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