Detecting social groups from space – assessment of remote sensing-based mapped morphological slums using income data

Remote Sensing Letters

Volumn 9, Issue 1, 2018, Pages 41-50

  Wurm, M ^a   Taubenbock H ^a  

^a GERMAN AEROSPACE CENTER DLR   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

HOUSING; INTERNATIONAL COOPERATION; MAPPING; POPULATION STATISTICS; SEMANTICS; SPACE OPTICS; SURVEYS;

GLOBAL POPULATION; IRREGULAR ARRANGEMENT; MORPHOLOGICAL CHARACTERISTIC; ORGANISATION FOR ECONOMIC CO-OPERATION AND DEVELOPMENT; PHYSICAL CHARACTERISTICS; SATELLITE IMAGES; SYSTEMATIC MAPPING; UNPLANNED SETTLEMENTS;

REMOTE SENSING;

ASSESSMENT METHOD; CENSUS; DETECTION METHOD; MAPPING METHOD; NEIGHBORHOOD; OECD; REMOTE SENSING; SATELLITE IMAGERY; SETTLEMENT PATTERN; SOCIOECONOMIC CONDITIONS; URBANIZATION;

BRAZIL; RIO DE JANEIRO [BRAZIL];

EID: 85042189340     PISSN: 2150704X     EISSN: 21507058     Source Type: Journal    
DOI: 10.1080/2150704X.2017.1384586     Document Type: Article

References (26)

1. Amnesty International. 2016. Eine Milliarde Menschen Leben in Slums. https://www.amnesty.de/mit-menschenrechten-gegen-armut/wohnen-wuerde/eine-milliarde-menschen-weltweit-leben-slums
2. Angeles, G., P. Lance, J. Barden-O’Fallon, N. Islam, A. Q. M. Mahbub, and N. I. Nazem. 2009. “The 2005 Census and Mapping of Slums in Bangladesh: Design, Select Results and Application.” International Journal of Health Geographics 8 (32): 8–32.
3. Arimah, B. C. 2010. “The Face of Urban Poverty.” UNU-WIDER, United Nations University (Working paper No. 2010/30). https://www.wider.unu.edu/sites/default/files/wp2010-30.pdf
4. Davis, M. 2006. Planet of Slums. London: Verso.
5. Engstrom, R., D. Newhouse, V. Haldavanekar, A. Copenhaver, and J. Hersh. 2017. “Evaluating the Relationship between Spatial and Spectral Features Derived from High Spatial Resolution Satellite Data and Urban Poverty in Colombo, Sri Lanka.” In Joint Urban Remote Sensing Event JURSE 2017, Dubai, UAE, 6-8 March.
6. Gruebner, O., J. Sachs, A. Nockert, M. Frings, M. H. Khan, T. Lakes, and P. Hostert. 2014. “Mapping the Slums of Dhaka from 2006 to 2010.” Dataset Papers in Science (2014). doi:10.1155/2014/172182.
7. Gunter, A. W. 2009. “Getting It for Free: Using Google EarthTM and ILWIS to Map Squatter Settlements in Johannesburg.” In IEEE International Geoscience and Remote Sensing Symposium IGARSS 2009, Cape Town, South Africa, 12-17 July; Volume 3, pp. 388–391.
8. Hofmann, P. 2001. “Detecting Informal Settlements from IKONOS Image Data Using Methods of Object Oriented Image analysis—An Example from Cape Town (South Africa).” In Proceedings of the 2nd international symposium remote sensing of urban areas (pp. 107–118). Regensburg, Germany, June 22–23.
9. IBGE (Instituto Brasileiro de Geografia e Estatistica). 2010. Base De Informacões Do Censo Demográfico: Resultados Do Universo Por Setor Censitário. Rio de Janeiro: IBGE.
10. Jean, N., M. Burke, M. Xie, W. M. Davis, D. B. Lobell, and S. Ermon. 2016. “Combining Satellite Imagery and Machine Learning to Predict Poverty.” Science 353 (6301): 790–794. doi:10.1126/science.aaf7894.
11. Klotz, M., M. Wurm, Z. Xiaoxiang, and H. Taubenböck. 2017. “Digital Deserts on the Ground and from Space.” In Joint Urban Remote Sensing Event JURSE 2017, Dubai, UAE, 6-8 March.
12. Kohli, D., A. Stein, and R. Sliuzas. 2016. “Uncertainty Analysis for Image Interpretations of Urban Slums.” Computers, Environment and Urban Systems 60: 37–49. doi:10.1016/j.compenvurbsys.2016.07.010.
13. Kuffer, M., K. Pfeffer, and R. Sliuzas. 2016. “Slums from Space—15 Years of Slum Mapping Using Remote Sensing.” Remote Sensing 8 (6): 455–464. doi:10.3390/rs8060455.
14. Kuffer, M., K. Pfeffer, R. Sliuzas, I. Baud, and Van Maarseveen. 2017. “Capturing the Diversity of Deprived Areas with Image-Based Features: The Case of Mumbai.“ Remote Sensing 9 (4): 384. doi:10.3390/rs9040384.
15. O’Hare, G., and M. Barke. 2002. “The Favelas of Rio De Janeiro: A Temporal and Spatial Analysis.” GeoJournal 56: 225–240.
16. OECD (Organization for Economic Co-operation and Development). 2017. Poverty Rate (Indicator). 10.1787/0fe1315d-en
17. Sandborn, A., and R. N. Engstrom. 2016. “Determining the Relationship between Census Data and Spatial Features Derived from High-Resolution Imagery in Accra, Ghana.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9 (5): 1970–1977. doi:10.1109/JSTARS.2016.2519843.
18. Sliuzas, R., G. Mboup, and A. De Sherbinin 2008. “Report of the Expert Group Meeting on Slum Identification and Mapping.” Report by CIESIN, UN-HABITAT, ITC.
19. Taubenböck, H., and N. Kraff. 2014. “The Physical Face of Slums: A Structural Comparison of Slums in Mumbai, India, Based on Remotely Sensed Data.” Journal of Housing and the Built Environment 29 (1): 15–38. doi:10.1007/s10901-013-9333-x.
20. Taubenböck, H., M. Wurm, N. Setiadi, N. Gebert, A. Roth, G. Strunz, J. Birkmann, and S. Dech. 2009. “Integrating Remote Sensing and Social Science – the Correlation of Urban Morphology with Socioeconomic Parameters.” In Joint Urban Remote Sensing Event JURSE 2009, Shanghai, PRC, 22-24 May
21. UN (United Nations). 2010. The Millennium Development Goals Report 2010. UN: New York.
22. UN-HABITAT. 2003. The challenge of slums: global report on Human settlements 2003. UN-Habitat: Nairobi.
23. UN-HABITAT. 2015. A Practical Guide to Designing, Planning, and Executing Citywide Slum Upgrading Programmes. Nairobi: UN-Habitat.
24. Usman, A., and A. Mahmood. 2011. “Urban Poverty, Household Income, and Location of Slums: Is There Any Linkage? A Study of Urban Slums in Karachi.” Journal of Applied and Emerging Sciences 2 (2): 150–155.
25. Wurm, M., M. Weigand, A. Schmitt, C. Geiß, and H. Taubenböck. 2017a. “Exploitation of Textural and Morphological Image Features in Sentinel-2A Data for Slum Mapping.” In Joint Urban Remote Sensing Event JURSE 2017, Dubai, UAE, 6-8 March.
26. Wurm, M., H. Taubenböck, M. Weigand, and A. Schmitt. 2017b. “Slum Mapping in Polarimetric SAR Data Using Spatial Features.” Remote Sensing of Environment 194: 190–204. doi:10.1016/j.rse.2017.03.030.