Mapping the Morphology of Urban Deprivation: The Role of Remote Sensing for Developing a Global Slum Repository

Abstract

Globally, about one billion urban dwellers live in deprived areas (commonly referred to as slums). However, this figure is highly uncertain due to large data gaps. For example, in many cities, systematic underreporting occurs, which hampers the monitoring of SDG indicators. Earth observation (EO) data can be used to extract consistent spatial information on important aspects of the physical domain of deprivation and can offer essential proxies to not well-covered (eg, social and economic) domains. However, for the development of a global data repository on deprived areas, several conceptual and methodological issues need to be solved. First, the relationship between concepts of a slum household and a deprived area needs to be defined in the context of information available in EO images. Second, the costs and benefits of different types of EO-data need to be established. Third, at different scales (ranging from communities, city to global scales), meaningful spatial aggregation units need to be established that are suitable to deal with uncertainties, privacy, ethics, and user needs. Fourth, computationally feasible, scalable, and transferable methods are required to produce a global data repository on deprived areas. This chapter provides an overview of methodological advances to address these four major challenges.