Uncertainties in analyzing the transferability of the generic slum ontology

Abstract

The Generic Slum Ontology (GSO) was developed to assist the detection of slums using Geographic Object-Based Image Analysis (GEOBIA). When applying the GSO locally, uncertainties exist in slum detection and transferability. Slums often have fuzzy boundaries and different ways to conceptualise. This study focuses on inherent uncertainties when analysing the transferability of the GSO across space, time and conceptualizations in the city of Jakarta, Indonesia. To measure the transferability of the GSO, we developed quantitative and qualitative indicators in multi-temporal Pleiades imagery (2012-2015) of two purposely-selected subsets. This framework allows assessing whether the developed ruleset is transferable across different spatial and temporal images. We applied two classification stages: background removal with a low scale parameter (SP) followed by slum extraction with a coarser SP. Both quantitative and qualitative indicators showed limited spatial and temporal transferability. Three sources of uncertainties can explain this result. First, the static concept of the employed ruleset and dynamic changes of slums. Real-world objects evolve over time, but their description remains static. Second, the gap between the real world (subjective conceptualization of objects) and image domain (quantitative values). For instance, the roof materials of slums (i.e. asbestos) have a similar spectral property with parking lot (from concrete), which resulted in misclassification. Third, the use of references data from local experts and municipal data introduce uncertainties that related to local ground knowledge and politics of slum declarations. Thus, this research contributes to the development of transferability measurements for the GSO and the understanding of underlying uncertainties.