The role of earth observation in an integrated deprived area mapping "system" for low-to-middle income countries
The role of earth observation in an integrated deprived area mapping "system" for low-to-middle income countries
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.
Deprived areas, Informal settlement, Machine learning, Slums, Urban remote sensing
Kuffer, Monika
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Thomson, Dana R.
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Boo, Gianluca
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Mahabir, Ron
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Grippa, Taïs
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Vanhuysse, Sabine
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Engstrom, Ryan
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Ndugwa, Robert
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Makau, Jack
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Darin, Edith
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de Albuquerque, João Porto
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Kabaria, Caroline
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18 March 2020
Kuffer, Monika
c8e67544-026c-411f-a08b-aaa926f30760
Thomson, Dana R.
1ad13f81-f22e-4d89-a288-b05fb08b6c39
Boo, Gianluca
d49f7aaa-6d95-4e36-b9be-e469911c4a3d
Mahabir, Ron
8adb388b-6da5-4577-863a-d2a86c0f5080
Grippa, Taïs
5cd12e5d-ecb5-429b-b527-99b1c1c2671b
Vanhuysse, Sabine
b14a29d0-693a-43e6-a5d9-a492eefd74d4
Engstrom, Ryan
5a337bab-b00e-404b-b98f-62a98bc232ed
Ndugwa, Robert
5a700daa-39bc-43fb-af1b-dc269e977011
Makau, Jack
48fd82cb-86b5-4623-aed1-a3dceb679e54
Darin, Edith
868fa688-2567-4dbd-aa12-3dcc91f2aa8d
de Albuquerque, João Porto
c31c3cd4-573e-44f5-949f-b402484a70b7
Kabaria, Caroline
e7e88d56-b332-4932-8531-04c16e9492c7
Kuffer, Monika, Thomson, Dana R., Boo, Gianluca, Mahabir, Ron, Grippa, Taïs, Vanhuysse, Sabine, Engstrom, Ryan, Ndugwa, Robert, Makau, Jack, Darin, Edith, de Albuquerque, João Porto and Kabaria, Caroline
(2020)
The role of earth observation in an integrated deprived area mapping "system" for low-to-middle income countries.
Remote Sensing, 12 (6), [982].
(doi:10.3390/rs12060982).
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, 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.
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More information
Accepted/In Press date: 11 March 2020
Published date: 18 March 2020
Keywords:
Deprived areas, Informal settlement, Machine learning, Slums, Urban remote sensing
Identifiers
Local EPrints ID: 439278
URI: http://eprints.soton.ac.uk/id/eprint/439278
ISSN: 2072-4292
PURE UUID: 859b2a4f-7bda-46dd-b595-afdae8a0c759
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Date deposited: 07 Apr 2020 16:32
Last modified: 01 Aug 2024 01:56
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Contributors
Author:
Monika Kuffer
Author:
Dana R. Thomson
Author:
Gianluca Boo
Author:
Ron Mahabir
Author:
Taïs Grippa
Author:
Sabine Vanhuysse
Author:
Ryan Engstrom
Author:
Robert Ndugwa
Author:
Jack Makau
Author:
João Porto de Albuquerque
Author:
Caroline Kabaria
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