Summary Globally, about one billion urban dwellers live in deprived areas (commonly referred as slums). However, this figure may be highly uncertain due to large data gaps. For example, in many cities, systematic underreporting occurs, which hampers the monitoring of Sustainable Development Goal (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 (e.g. 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 the above four major challenges .
|Title of host publication||Urban Remote Sensing|
|Subtitle of host publication||Monitoring, Synthesis, and Modeling in the Urban Environment|
|Number of pages||19|
|Publication status||Published - 2022|
- deprived area
- machine learning