Detecting informal settlements from QuickBird data in Rio de Janeiro using an object-based approach

Informal settlements behave very dynamical over space and time and the number of people living in such housing areas is growing worldwide. The reasons for this dynamical behavior are manifold and are not matter of this article. Nevertheless, informal settlements represent a status quo of housing and living conditions which is from a humanitarian point of view in the most cases below acceptable levels. Therefore, reliable spatial information about informal settlements is vital for any actions of improvement of these living conditions. Since remote sensing data is a well suited data source for mapping and monitoring we demonstrate a methodology to detect informal settlements (favelas) from QuickBird data using an object-based approach. On the one hand we therefore use experiences and adapt them which were already made by Hofmann, P. (2001) regarding the image segmentation of an IKONOS scene of Cape Town. On the other hand we resort to a general ontology of informal settlements which we then transfer to a fuzzy-logic rule base which acts as basic classifier of the generated segments. This basic rule base is than extended in a way that features of segregation given by the ontology (namely neighbour hood) are applied to the extraction method as an iterative process (i.e. a knowledge based region growing). Finally, we assess the results of the simple and iterative method by comparing them with the results of a manual mapping. © 2008, Springer Berlin Heidelberg. All rights reserved.