The amount of scientific literature on (Geographic) Object-based Image Analysis – GEOBIA has been and still is sharply increasing. These approaches to analysing imagery have antecedents in earlier research on image segmentation and use GIS-like spatial analysis within classification and feature extraction approaches. This article investigates these development and its implications and asks whether or not this is a new paradigm in remote sensing and Geographic Information Science (GIScience). We first discuss several limitations of prevailing per-pixel methods when applied to high resolution images. Then we explore the paradigm concept developed by Kuhn (1962) and discuss whether GEOBIA can be regarded as a paradigm according to this definition. We crystallize core concepts of GEOBIA, including the role of objects, of ontologies and the multiplicity of scales and we discuss how these conceptual developments support important methods in remote sensing such as change detection and accuracy assessment. The ramifications of the different theoretical foundations between the ‘per-pixel paradigm’ and GEOBIA are analysed, as are some of the challenges along this path from pixels, to objects, to geo-intelligence. Based on several paradigm indications as defined by Kuhn and based on an analysis of peer-reviewed scientific literature we conclude that GEOBIA is a new and evolving paradigm.

Object-based image analysis (OBIA) has become very popular since the turn of the century. For high-resolution situations, in particular, where the objects of interest are larger than pixels, methods have been developed that build on image segmentation and on the further classification of objects rather than on pixels. Many studies have shown that OBIA methods are, in principle, more transferable and reapplicable to other images. To obtain comparable results by reapplying a given rule set on (slightly) changed conditions, the rule set must either be able to adapt to the changed conditions or it must be parameterized for manual adaptation. In this context, a rule set can be seen as the more robust the less it has to be changed, and vice versa. In this article we introduce a new method to evaluate the robustness of a rule set. The main assumption is that the amount of necessary adaptations can be measured in conjunction with the quality of classification achieved. We demonstrate that the method introduced is able to (1) evaluate the robustness of a rule set and (2) identify crucial elements of a rule set that need to be reparameterized.

The increasing development of informal settlements and slums in the years to come is a humanitarian challenge we are facing worldwide. The reasons for this development are mainly given by an urbanization of poverty and the lack of affordable housing in urban areas. Consequently, the majority of slum dwellers are forced to live in housing conditions which are simply unacceptable. In order to fight urban poverty and develop strategies to reduce the number of slum dwellers it is essential to inventory and monitor slums and to understand the underlying mechanisms of slum genesis. However, systematic spatial knowledge about these mechanisms is still absent. This paper addresses the capabilities of mapping and structural analysis of informal settlements using remote sensing data and modelling their genesis based on agent based approaches.