Image based classification of slums, built-up and non-built-up areas in Kalyan and Bangalore, India

Elena Ranguelova* (Corresponding Author), Berend Weel, M. Kuffer, K. Pfeffer, Michael Lees, Debraj Roy

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

13 Citations (Scopus)
84 Downloads (Pure)


Slums, characterized by sub-standard housing conditions, are a common in fast growing Asian cities. However, reliable and up-to-date information on their locations and development dynamics is scarce. Despite numerous studies, the task of delineating slum areas remains a challenge and no general agreement exists about the most suitable method for detecting or assessing detection performance. In this paper, standard computer vision methods–Bag of Visual Words framework and Speeded-Up Robust Features have been applied for image-based classification of slum and non-slum areas in Kalyan and Bangalore, India, using very high resolution RGB images. To delineate slum areas, image segmentation is performed as pixel-level classification for three classes: Slums, Built-up and Non-Built-up. For each of the three classes, image tiles were randomly selected using ground truth observations. A multi-class support vector machine classifier has been trained on 80% of the tiles and the remaining 20% were used for testing. The final image segmentation has been obtained by classification of every 10th pixel followed by a majority filtering assigning classes to all remaining pixels. The results demonstrate the ability of the method to map slums with very different visual characteristics in two very different Indian cities.

Original languageEnglish
Pages (from-to)1-23
Number of pages23
JournalEuropean Journal of Remote Sensing
Publication statusE-pub ahead of print/First online - 3 Nov 2018


  • bag of visual words
  • Image segmentation
  • informal settlements
  • speeded-up robust features
  • support vector machines


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