Mapping informal settlement indicators using object-oriented analysis in the Middle East

Mapping informal settlements is crucial for resource and utility management and planning. In 2003, the UN-Habitat developed a process for mapping and monitoring urban inequality to support reporting against the sustainable development goals (SDGs). Informal settlement indicators are used as a framework to carry out image analysis, and include vegetation extent, lacunarity of housing structures / vacant land, road segment type and materials, texture measures of built-up areas, roofing extent of built-up areas and dwelling size. Object-based image analysis (OBIA) methods are recommended to identify informal settlements. This paper documents the application of OBIA to map informal settlements, drawing on the ontology of Kohli et al. (2012) and the indicators of Owen and Wong (2013) for a Middle Eastern city. Three informal settlements with different land use histories were selected to represent old and new informal settlements in the city of Jeddah, Saudi Arabia. Vegetation extent was the most successful indicator detected, with 100% producer accuracy and over 84% user accuracy, followed by the road network, with 84% producer and user accuracies in older informal settlements and 73% producer accuracy and 96% user accuracy across all case studies. Lacunarity of housing structures / vacant land was detected well in informal settlements. The texture measure indicator was detected using with low producer accuracy across all case studies. The roofing extent of the built-up area is detected with better producer and user accuracies than texture measures. The dwellings size indicator generally failed to distinguish formal from informal settlements. Informal and formal were distinguished with an overall accuracy of 83%. This research concludes that OBIA is a useful method to map informal settlement indicators in Middle Eastern cities. However, a generic ruleset for mapping informal settlements remains elusive, and each indicator requires significant localised 'tuning'