Mapping the morphology of urban deprivation

M. Kuffer, Taïs Grippa, C. Persello, Hannes Taubenböck, K. Pfeffer, R. Sliuzas

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

Abstract

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 .
Original languageEnglish
Title of host publicationUrban Remote Sensing
Subtitle of host publication Monitoring, Synthesis, and Modeling in the Urban Environment
EditorsXiaojun Yang
PublisherWiley
Chapter14
Pages305-323
Number of pages19
ISBN (Print)9781119625865
DOIs
Publication statusPublished - 2022

Keywords

  • deprived area
  • slum
  • machine learning
  • deep-learning
  • CNNs

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