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The Role of Earth Observation in an Integrated Deprived Area Mapping "System" for Low-to-Middle Income Countries

[journal article]

Kuffer, Monika
Thomson, Dana R.
Boo, Gianluca
Mahabir, Ron
Grippa, Taïs
Vanhuysse, Sabine
Engstrom, Ryan
Robert, Ndugwa
Makau, Jack
Darin, Edith
Albuquerque, João Porto de
Kabaria, Caroline

Abstract

Urbanization in the global South has been accompanied by the proliferation of vast informal and marginalized urban areas that lack access to essential services and infrastructure. UN-Habitat estimates that close to a billion people currently live in these deprived and informal urban settlements, gen... view more

Urbanization in the global South has been accompanied by the proliferation of vast informal and marginalized urban areas that lack access to essential services and infrastructure. UN-Habitat estimates that close to a billion people currently live in these deprived and informal urban settlements, generally grouped under the term of urban slums. Two major knowledge gaps undermine the efforts to monitor progress towards the corresponding sustainable development goal (i.e., SDG 11 - Sustainable Cities and Communities). First, the data available for cities worldwide is patchy and insufficient to differentiate between the diversity of urban areas with respect to their access to essential services and their specific infrastructure needs. Second, existing approaches used to map deprived areas (i.e., aggregated household data, Earth observation (EO), and community-driven data collection) are mostly siloed, and, individually, they often lack transferability and scalability and fail to include the opinions of different interest groups. In particular, EO-based-deprived area mapping approaches are mostly top-down, with very little attention given to ground information and interaction with urban communities and stakeholders. Existing top-down methods should be complemented with bottom-up approaches to produce routinely updated, accurate, and timely deprived area maps. In this review, we first assess the strengths and limitations of existing deprived area mapping methods. We then propose an Integrated Deprived Area Mapping System (IDeAMapS) framework that leverages the strengths of EO- and community-based approaches. The proposed framework offers a way forward to map deprived areas globally, routinely, and with maximum accuracy to support SDG 11 monitoring and the needs of different interest groups.... view less

Keywords
microcensus; slum; deprivation; settlement; learning; urbanization; data capture; observation

Classification
Sociology of Settlements and Housing, Urban Sociology
Methods and Techniques of Data Collection and Data Analysis, Statistical Methods, Computer Methods

Free Keywords
deprived areas; informal settlement; machine learning; urban remote sensing

Document language
English

Publication Year
2020

Page/Pages
p. 1-26

Journal
Remote Sensing, 12 (2020) 6

DOI
https://doi.org/10.3390/rs12060982

ISSN
2072-4292

Status
Published Version; peer reviewed

Licence
Creative Commons - Attribution 4.0


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