Towards The Collection of Census Data From Satellite Imagery Using Data Mining: A Study With Respect to the Ethiopian Hinterland

  • Kwankamon Dittakan
  • Frans Coenen
  • Rob Christley
Conference paper


The collection of census data is an important task with respect to providing support for decision makers. However, the collection of census data is also resource intensive. This is especially the case in areas which feature poor communication and transport networks. In this paper a method is proposed for collecting census data by applying classification techniques to relevant satellite imagery. The test site for the work is a collection of villages lying some 300km to the northwest of Addis Ababa in Ethiopia. The idea is to build a classifier that can label households according to “family” size. To this end training data has been obtained, by collecting on ground census data and aligning this up with satellite data. The fundamental idea is to segment satellite images so as to obtain the satellite pixels describing individual households and representing these segmentations using a histogram representation. By pairing each histogram represented household with collated census data, namely family size, a classifier can be constructed to predict household sizes according to the nature of the histograms. This classifier can then be used to provide a quick and easy mechanism for the approximate collection of census data that does not require significant resource.


Feature Selection Satellite Image Census Data Information Gain Satellite Imagery 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Otto Bretscher. Linear Algebra with Applications (3rd Edition). Prentice Hall, July 2004.Google Scholar
  2. 2.
    C. R. Brice and C. L. Fennema. Scene Analysis Using Regions. Artificial Intelligence, 1(3):205–226, 1970.Google Scholar
  3. 3.
    John Canny. A computational approach to edge detection. Pattern Analysis and Machine Intelligence, IEEE Transactions on, PAMI-8(6):679 –698, nov. 1986.Google Scholar
  4. 4.
    Jin-Song DENG, Ke WANG, Jun LI, and Yan-Hua DENG. Urban land use change detection using multisensor satellite images. Pedosphere, 19(1):96 – 103, 2009.CrossRefGoogle Scholar
  5. 5.
    Richard O. Duda and Peter E. Hart. Use of the hough transformation to detect lines and curves in pictures. Commun. ACM, 15(1):11–15, January 1972.Google Scholar
  6. 6.
    J. Faichney and R. Gonzalez. Combined colour and contour representation using anti-aliased histograms. In Signal Processing, 2002 6th International Conference on, volume 1, pages 735 – 739 vol.1, aug. 2002.Google Scholar
  7. 7.
    Juan A. X. Fanoe. Lessons from census taking in south africa: Budgeting and accounting experiences. The African Statistical, 13(3):82–109, 2011.Google Scholar
  8. 8.
    Rafael C. Gonzalez and Richard E. Woods. Digital Image Processing (3rd Edition). Prentice Hall, 3 edition, August 2007.Google Scholar
  9. 9.
    E. L. Hall. Almost uniform distributions for computer image enhancement. IEEE Trans. Comput., 23(2):207–208, February 1974.MATHCrossRefGoogle Scholar
  10. 10.
    Wynne Hsu, S. T. Chua, and H. H. Pung. An integrated color-spatial approach to content-based image retrieval. In Proceedings of the third ACM international conference on Multimedia, MULTIMEDIA ’95, pages 305–313, New York, NY, USA, 1995. ACM.Google Scholar
  11. 11.
    Jacek Kozak, Christine Estreguil, and Katarzyna Ostapowicz. European forest cover mapping with high resolution satellite data: The carpathians case study. International Journal of Applied Earth Observation and Geoinformation, 10(1):44 – 55, 2008.CrossRefGoogle Scholar
  12. 12.
    Tzu-Chuen Lu and Chin-Chen Chang. Color image retrieval technique based on color features and image bitmap. Inf. Process. Manage., 43(2):461–472, March 2007.Google Scholar
  13. 13.
    G. Mallinis, I.D. Mitsopoulos, A.P. Dimitrakopoulos, I.Z. Gitas, and M. Karteris. Local-scale fuel-type mapping and fire behavior prediction by employing high-resolution satellite imagery.Google Scholar
  14. 14.
    Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of, 1(4):230 –239, dec. 2008.Google Scholar
  15. 15.
    V. Mesev. The use of census data in urban image classification. PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING, (5):431–438, May 1998.Google Scholar
  16. 16.
    F. O’Gorman and M. B. Clowes. Finding picture edges through collinearity of feature points. IEEE Trans. Comput., 25(4):449–456, April 1976.MATHCrossRefGoogle Scholar
  17. 17.
    Nobuyuki Otsu. A Threshold Selection Method from Gray-level Histograms. IEEE Transactions on Systems, Man and Cybernetics, 9(1):62–66, 1979.Google Scholar
  18. 18.
    N. Papamarkos and B. Gatos. A new approach for multilevel threshold selection. CVGIP: Graph. Models Image Process., 56(5):357–370, September 1994.CrossRefGoogle Scholar
  19. 19.
    T. Pavlidis. Algorithms for Graphics and Image Processing. Springer, 1982. 19. K. Roychowdhury, S. Jones, C. Arrowsmith, and K. Reinke. Indian census using satellite images: Can dmsp-ols data be used for small administrative regions? In Urban Remote Sensing Event (JURSE), 2011 Joint, pages 153 –156, april 2011.Google Scholar
  20. 20.
    Xiang-YangWang, Jun-FengWu, and Hong-Ying Yang. Robust image retrieval based on color histogram of local feature regions. Multimedia Tools Appl., 49(2):323–345, August 2010.CrossRefGoogle Scholar
  21. 21.
    Ian H.Witten, Eibe Frank, and Mark A. Hall. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, Amsterdam, 3. edition, 2011.Google Scholar
  22. 22.
    Yang Zhang, Rongyi He, and Muwei Jian. Comparison of two methods for texture image clasification. In Proceedings of the 2009 Second International Workshop on Computer Science and Engineering - Volume 01, IWCSE ’09, pages 65–68, Washington, DC, USA, 2009. IEEE Computer Society.Google Scholar
  23. 23.
    S. W. Zucker and D. Terzopoulos. Finding structure in co-occurrence matrices for texture analysis. Computer Graphics and Image Processing, 12:286–308, 1980.Google Scholar
  24. 24.
    Reyer Zwiggelaar. Texture based segmentation: Automatic selection of co-occurrence matrices. In Proceedings of the Pattern Recognition, 17th International Conference on (ICPR’04) Volume 1 - Volume 01, ICPR ’04, pages 588–591, Washington, DC, USA, 2004. IEEE ComputerSociety.Google Scholar

Copyright information

© Springer-Verlag London 2012

Authors and Affiliations

  • Kwankamon Dittakan
    • 1
  • Frans Coenen
    • 1
  • Rob Christley
    • 2
  1. 1.Department of Computer ScienceUniversity of LiverpoolLiverpoolUK
  2. 2.Institute of Infection and Global HealthUniversity of LiverpoolNestonUK

Personalised recommendations