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Integrating high-resolution remote sensing and street view images to identify urban villages： A case study in Yuexiu District， Guangzhou City

  • role： First author第一作者
  • Affiliation：

    State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, China

  • Email：cuic@lreis.ac.cn
  • Introduction：崔成，研究方向为空间数据挖掘。E-mail： cuic@lreis.ac.cn
CUI Cheng12,  
  • Affiliation：

    State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, China

ZHAO Lu12,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

  • Email：renhy@lreis.ac.cn
  • Introduction：任红艳，研究方向为环境遥感与公共健康。E-mail： renhy@lreis.ac.cn
REN Hongyan1*,  
  • Affiliation：

    State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, China

LU Weili12,  
  • Affiliation：

    State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, China

HUANG Yaohuan12

résumé

China has been experiencing rapid urbanization at an unprecedented rate with the significantly changing urban internal spatial structure. As an inevitable byproduct, Urban Villages (UVs), which refer to informal living spaces with substandard living conditions, have emerged in many newly and quickly industrialized regions and cities. Although UVs provide plenty of living spaces for floating populations, their poor living environment has a negative impact on the urban landscape and public health. Thus, obtaining the spatial distribution and environmental quality information of UVs in a timely manner and accurately for optimizing urban spaces and improving human settlements has practical significance. High-resolution Remote Sensing Images (RSIs) and Street View Images (SVIs) have been employed to quickly extract UV information. However, the combination of RSIs and SVIs for retrieving UV information has received little attention. In this study, we took Yuexiu District in Guangzhou City as the study area and then propose a UV identification method based on the GF-2 high-resolution RSIs and SVIs released by Baidu Company. First, street space quality information was derived from the SVIs using support vector machine and random forest. Then, on the basis of the pre-extraction results on the GF-2 images, multi-scale segmentation was performed based on object-based image analysis, including the building instance and block levels. Twenty-three features were obtained, including the spectrum, shape, texture, building structure, and scene from the RSIs, and five indicators were obtained to measure the street space quality on the basis of the SVIs. Finally, the random forest algorithm was applied to combine the features of the two kinds of images to identify the UVs. Experimental results demonstrate that the UV recognition based on RSIs has an overall accuracy of 94.5% and a Kappa coefficient of 0.58, and the overall accuracy and Kappa coefficient of the UV identification based on SVIs are 85.7% and 0.31, respectively. An overall accuracy of 96.1% and a Kappa coefficient of 0.67 were achieved by the fusion model of the two kinds of images, exhibiting the best performance in UV recognition. Street space quality, textural, structural, and shape features play an import role in the UV recognition based on the fusion model of RSIs and SVIs. The five indicators that measure street space quality on the basis of SVIs contributed 31.6% in feature importance to the fusion model. The information provided by RSIs from the bird view and the SVIs from the human perspective could complement each other, creating an outstanding feature space and reducing the misclassification phenomenon of UVs. The key to this method is integrating the information provided by SVIs into the UV extraction process based on high-resolution RSIs to obtain a highly stable and reliable UV classification result in Yuexiu District. Multi-source data fusion is an important method in improving the ability of RSIs, and other data should be collected to enrich the existing coupling methods and the technical system. This paper reveals that the fusion of high-resolution RSIs and SVIs in the feature level could improve the recognition accuracy of UVs, and the extracted UV distribution data can be used in urban planning and other studies related to urban development. The information in SVIs could be integrated into high-resolution RSIs and other data sources to assist in identifying informal living spaces, such as UVs. Therefore, retrieving highly accurate UV information is feasible through the combination of RSIs and SVIs.

mots-clés

high-resolution remote sensing image;street view image;urban village;street space quality;Random Forest;image fusion;multi-scale segmentation

References

  1. 1.
    Anguelov D, Dulong C, Filip D, Frueh C, Lafon S, Lyon R, Ogale A, Vincent L and Weaver J. 2010. Google street view: capturing the world at street level. Computer, 43(6): 32-38
  2. 2.
    Barbierato E, Bernetti I, Capecchi I and Saragosa C. 2020. Integrating remote sensing and street view images to quantify urban forest ecosystem services. Remote Sensing, 12(2): 329
  3. 3.
    Breiman L. 2001, Random forests. Machine learning, 2001, 45(1): 5-32
  4. 4.
    Blasch E P and Huang S H. 2000. Multilevel feature-based fuzzy fusion for target recognition. Proceedings of SPIE -The International Society for Optical Engineering, 4051:279-288
  5. 5.
    Cao R, Tu W, Yang C X, Li Q, Liu J, Zhu J S, Zhang Q, Li Q Q and Qiu G P. 2020. Deep learning-based remote and social sensing data fusion for urban region function recognition. ISPRS Journal of Photogrammetry and Remote Sensing, 163: 82-97
  6. 6.
    Cui C, Ren H Y, Zhao L and Zhuang D F. 2020. Street space quality evaluation in Yuexiu District of Guangzhou City based on multi-feature fusion of street view imagery. Journal of Geo-Information Science, 22(6): 1330-1338
  7. 7.
    D’Oleire-Oltmanns S, Coenradie B and Kleinschmit B. 2011. An object-based classification approach for mapping Migrant housing in the mega-urban area of the Pearl River Delta (China). Remote Sensing, 3(8): 1710-1723
  8. 8.
    Gan X Y, She T W and Long Y. 2018. Understanding urban informality in street built environment combining manual evaluation with machine learning in processing the Beijing old city’s street-view images. Time Architecture, (1): 62-68
  9. 9.
    Ghasempour A. 2015. Informal settlement; concept, challenges and intervention approaches. Specialty Journal of Architecture and Construction, 1(3): 10-16
  10. 10.
    Guo H X, Huang Y and Zhao D Q. 2013. Modeling of spatial distribution of urban population density: a case study of Tianhe district, Guangzhou. Tropical Geography, 33(1): 81-87.
  11. 11.
    Hao P, Geertman S, Hooimeijer P and Sliuzas R. 2013. Spatial analyses of the urban village development process in Shenzhen, China. International Journal of Urban and Regional Research, 37(6): 2177-2197
  12. 12.
    He L, Páez A and Liu D S. 2017. Built environment and violent crime: an environmental audit approach using Google Street View. Computers, Environment and Urban Systems, 66: 83-95
  13. 13.
    Hoffmann E J, Wang Y Y, Werner M, Kang J and Zhu X X. 2019. Model fusion for building type classification from aerial and street view images. Remote Sensing, 11(11): 1259
  14. 14.
    Hofmann P, Strobl J, Blaschke T and Kux H. 2008. Detecting informal settlements from Quick Bird data in Rio de Janeiro using an object-based approach. Object-Based image analysis. Berlin, Heidelberg: Springer: 531-533
  15. 15.
    Hu T Y, Yang J, Li X C and Gong P. 2016. Mapping urban land use by using Landsat Images and open social data. Remote Sensing, 8(2): 151
  16. 16.
    Huang X, Liu H and Zhang L P. 2015. Spatiotemporal detection and analysis of urban villages in mega city regions of China using high-resolution remotely sensed imagery. IEEE Transactions on Geoscience and Remote Sensing, 53(7): 3639-3657
  17. 17.
    Jia Y X, Ge Y, Ling F, Guo X, Wang J H, Wang L, Chen Y H and Li X D. 2018. Urban land use mapping by combining remote sensing imagery and mobile phone positioning data. Remote Sensing, 10(3): 446
  18. 18.
    Jing R, Gong Z N, Zhu W D, Guan H L, Zhao W J and Zhang T. 2020. Extraction of buildings from remote sensing imagery based on multi-scale SLIC-GMRF and FCNSVM. Journal of Remote Sensing, 24(1): 11-26
  19. 19.
    Kohli D, Warwadekar P, Kerle N, Sliuzas R and Stein A. 2013. Transferability of object-oriented image analysis methods for slum identification. Remote Sensing, 5(9): 4209-4228
  20. 20.
    Kuffer M, Pfeffer K and Sliuzas R. 2016. Slums from space—15 years of slum mapping using remote sensing. Remote Sensing, 8(6): 455
  21. 21.
    Li L X. 2005. The social and economic characteristics of urban village: a case study of typical urban village in Guangzhou. Beijing Planning Review, 12(3): 34-37
  22. 22.
    Li P L. 2002. Tremendous changes: the end of villages-A study of villages in the center of Guangzhou city. Social Sciences in China, (1): 168-179
  23. 23.
    Li X J, Zhang C R, Li W D, Ricard R, Meng Q Y and Zhang W X. 2015. Assessing street-level urban greenery using Google Street View and a modified green view index. Urban Forestry and Urban Greening, 14(3): 675-685
  24. 24.
    Li Z G and Wu F L. 2013. Residential satisfaction in China’s informal settlements: a case study of Beijing, Shanghai, and Guangzhou. Urban Geography, 34(7): 923-949
  25. 25.
    Lin W S, Feng J and Li Y. 2018. Influence of ICT on housing and employment related migration space of residents in urban villages: a case study of five urban villages in Beijing. Progress in Geography, 37(2): 276-286
  26. 26.
    Liu H. 2018. Study of Urban Village Detection Methods Based on High-Resolution Remote Sensing Imagery. Wuhan: Wuhan University
  27. 27.
    Liu L, Silva E A, Wu C Y and Wang H. 2017a. A machine learning-based method for the large-scale evaluation of the qualities of the urban environment. Computers, Environment and Urban Systems, 65: 113-125
  28. 28.
    Liu X P, He J L, Yao Y, Zhang J B, Liang H L, Wang H and Hong Y. 2017b. Classifying urban land use by integrating remote sensing and social media data. International Journal of Geographical Information Science, 31(8): 1675-1696
  29. 29.
    Liu Y T, He S J, Wu F L and Webster C. 2010. Urban villages under China’s rapid urbanization: unregulated assets and transitional neighbourhoods. Habitat International, 34(2): 135-144
  30. 30.
    Lu B, Wei X K and Bi D Y. 2005. Application of Support Vector Machine in Classification. Journal of Image and Graphics, 10(8):1029-1035
  31. 31.
    Ministry of Housing and Urban-Rural Development of the People’s Republic of China. 2016. GJJ37-2012(2016): Code for Design of Urban Road Engineering
  32. 32.
    Ren H Y, Wu W, Li T G and Yang Z C. 2019. Urban villages as transfer stations for dengue fever epidemic: a case study in the Guangzhou, China. PLoS Neglected Tropical Diseases, 13(4): e0007350
  33. 33.
    Song J C, Lin T, Li X H and Prishchepov A V. 2018. Mapping urban functional zones by integrating very high spatial resolution remote sensing imagery and points of interest: a case study of Xiamen, China. Remote Sensing, 10(11): 1737
  34. 34.
    Song M H. 2019. Object-oriented urban land classification with GF-2 remote sensing image. Remote Sensing Technology and Application , 34(3): 547-552, 629
  35. 35.
    Tan K, Zhang Y S, Wang X and Chen Y. 2019. Object-based change detection using multiple classifiers and multi-scale uncertainty analysis. Remote Sensing, 11(3): 359
  36. 36.
    Tong D, Feng C C and Deng J J. 2011. Spatial evolution and cause analysis of urban villages: a case study of Shenzhen Special Economic Zone. Geographical Research, 30(3): 437-446
  37. 37.
    Wang S G and Wang Y. 2009. Shadow detection and compensation in high resolution satellite image based on retinex. Proceedings of the 5th International Conference on Image and Graphics. Xi’an, China: IEEE 2009:209-212
  38. 38.
    Ye Y, Zhang Z X, Zhang X H and Zeng W. 2019. Human-scale quality on streets: a large-scale and efficient analytical approach based on street view images and new urban analytical tools. Urban Planning International, 34(1): 18-27
  39. 39.
    Zhang L P and Shen H F. 2016. Progress and future of remote sensing data fusion. Journal of Remote Sensing, 20(5): 1050-1061
  40. 40.
    Zhang L Y, Pei T, Chen Y J, Song C and Liu X Q. 2019. A review of urban environmental assessment based on street view images. Journal of Geo-Information Science, 21(1): 46-58
  41. 41.
    Zhao C Y. 2018. Restoration and Classification of Remote Sensing Imagery Based on Multiple Feature Learning. Xi’an: Xidian University
  42. 42.
    Zhao L, Ren H, Cui C, Huang Y. 2020. A Partition-Based Detection of Urban Villages Using High-Resolution Remote Sensing Imagery in Guangzhou, China. Remote Sensing. 12(14):2334
  43. 43.
    Zhao Y H, Chen G Q, Chen G L, Liu X P and Niu N. 2018. Integrating multi-source big data to extract buildings of urban villages: a case study of Tianhe district, Guangzhou. Geography and Geo-Information Science, 34(5): 7-13

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