The role of earth observation in an integrated deprived area mapping 'system' for low-to-middle income countries
| Autor: | Gianluca Boo, Robert Ndugwa, Caroline W Kabaria, Dana R. Thomson, Ryan Engstrom, Taïs Grippa, João Porto de Albuquerque, Edith Darin, Jack Makau, Monika Kuffer, Sabine Vanhuysse, Ron Mahabir |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Beobachtung
HD Earth observation observation 010504 meteorology & atmospheric sciences 0211 other engineering and technologies HA deprived areas informal settlement machine learning urban remote sensing Benachteiligung Urban remote sensing 02 engineering and technology Deprived areas 01 natural sciences Sociology & anthropology deprivation HT HV Mikrozensus Urbanisierung lcsh:Science QA media_common Sozialwissenschaften Soziologie learning Slum Informal settlement GF Geography Scalability ddc:300 ddc:301 media_common.quotation_subject Physique de l'état solide urbanization Lernen slums Siedlung settlement Urbanization Human settlement Sociology of Settlements and Housing Urban Sociology Machine learning microcensus Datengewinnung Environmental planning Social sciences sociology anthropology 021101 geological & geomatics engineering 0105 earth and related environmental sciences Sustainable development Erhebungstechniken und Analysetechniken der Sozialwissenschaften Data collection Métallurgie Slums Siedlungssoziologie Stadtsoziologie data capture Methods and Techniques of Data Collection and Data Analysis Statistical Methods Computer Methods Soziologie Anthropologie Mapping system ITC-ISI-JOURNAL-ARTICLE General Earth and Planetary Sciences lcsh:Q ITC-GOLD Diversity (politics) |
| Zdroj: | Remote Sensing, 12 (6 Remote Sensing Remote Sensing, Vol 12, Iss 6, p 982 (2020) |
| ISSN: | 2072-4292 |
| Popis: | 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. SCOPUS: re.j info:eu-repo/semantics/published |
| Databáze: | OpenAIRE |
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