Abstract
Updated spatial information on the dynamics of slums can be helpful to measure and evaluate progress of policies. Earlier studies have shown that semi-automatic detection of slums using remote sensing can be challenging considering the large variability in definition and appearance. In this study, we explored the potential of an object-oriented image analysis (OOA) method to detect slums, using very high resolution (VHR) imagery. This method integrated expert knowledge in the form of a local slum ontology. A set of image-based parameters was identified that was used for differentiating slums from non-slum areas in an OOA environment. The method was implemented on three subsets of the city of Ahmedabad, India. Results show that textural features such as entropy and contrast derived from a grey level co-occurrence matrix (GLCM) and the size of image segments are stable parameters for classification of built-up areas and the identification of slums. Relation with classified slum objects, in terms of enclosed by slums and relative border with slums was used to refine classification. The analysis on three different subsets showed final accuracies ranging from 47% to 68%. We conclude that our method produces useful results as it allows including location specific adaptation, whereas generically applicable rulesets for slums are still to be developed.
Highlights
The proliferation of slums in cities in developing countries is a major concern for local, national and international organizations
For Subset-1, the parameters GLCMCon(B), enclosedness by Slums (ES), RB(SL) and Area were used for classification of slums
The slums in the vicinity of the river were classified with 47% accuracy after clean-up (Figure 7)
Summary
The proliferation of slums in cities in developing countries is a major concern for local, national and international organizations. Access to reliable spatial and other data on slums is essential for assessing the performance of policies and programs for slum eradication. Slums are frequently omitted from formal statistical assessments, current spatial information on the concentration or location of slum dwellers is frequently absent [5,6]. Other methods, such as participatory approaches, require the involvement of local people and are time-consuming and resource-intensive [7]. With the increasing availability of very high resolution (VHR) satellite imagery, we hypothesize that detection and characterization of slum identification can be improved
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