Robust monitoring approaches for informal settlements using very high-resolution (VHR) satellite imagery can deliver essential
information for supporting the formulation of pro-poor policies. Such information can complement census methods or participatory
approaches. With the increasing availability of VHR satellite imagery, detection of the informal settlements benefits from the
conceptualization of location-specific knowledge in the form of a locally-adapted generic slum ontology (GSO). In this study, we
developed the local slum ontology for Mumbai, India, by incorporating local knowledge with image-based proxies. Then, we
translated the local ontology into a rule set using Object Based Image Analysis (OBIA) to identify informal settlements by using
spectral, spatial, geometric and texture measures. The method was applied to three subsets of a Worldview-2 imagery. The
robustness of the initial rule set was analysed with the help of membership functions. The results showed that the normalized
difference ratio of near infrared (NIR) and blue band and grey level co-occurrence matrix (GLCM) features are most effective in all
three subsets in the identification of informal settlements. The results suggest that the rule sets developed in this study can potentially
be applied to other study areas of Worldview-2 imagery for informal settlements identification.