Urban slum detection using texture and spatial metrics derived from satellite imagery

Abstract

Slum detection from satellite imagery is challenging due to the variability in slum types and definitions. This research aimed at developing a method for slum detection based on the morphology of the built environment. The method consists of segmentation followed by hierarchical classification using object-oriented image analysis and integrating expert knowledge in the form of a local slum ontology. Results show that textural feature contrast derived from a grey-level co-occurrence matrix was useful for delineating segments of slum areas or parts thereof. Spatial metrics such as the size of segments and proportions of vegetation and built-up were used for slum detection. The percentage of agreement between the reference layer and slum classification was 60 percent. This is lower than the accuracy achieved for land cover classification (80.8 percent), due to large variations. We conclude that the method produces useful results and has potential for successful application in contexts with similar morphology.

Keywords:

• Slums
• ontology
• informal settlements
• remote sensing
• image classification
• texture
• spatial metrics
• object-oriented image analysis (OOA)
• Pune, India

Acknowledgements

We would like to thank MASHAL, Pune for providing data for this research. We would also like to thank Dr. Sulochana Sekhar for her valuable advice and assistance in field-work.