GI_Forum 2021, Volume 9, Issue 1 12th International Symposium on Digital Earth
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Verlag der Österreichischen Akademie der Wissenschaften Austrian Academy of Sciences Press
A-1011 Wien, Dr. Ignaz Seipel-Platz 2
Tel. +43-1-515 81/DW 3420, Fax +43-1-515 81/DW 3400 https://verlag.oeaw.ac.at, e-mail: verlag@oeaw.ac.at |
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DATUM, UNTERSCHRIFT / DATE, SIGNATURE
BANK AUSTRIA CREDITANSTALT, WIEN (IBAN AT04 1100 0006 2280 0100, BIC BKAUATWW), DEUTSCHE BANK MÜNCHEN (IBAN DE16 7007 0024 0238 8270 00, BIC DEUTDEDBMUC)
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GI_Forum 2021, Volume 9, Issue 1 12th International Symposium on Digital Earth
ISSN 2308-1708 Online Edition ISBN 978-3-7001-8947-3 Online Edition
Monika Kuffer,
Sabine Vanhuysse,
Stefanos Georganos,
Jon Wang
S. 85 - 93 doi:10.1553/giscience2021_01_s85 Verlag der Österreichischen Akademie der Wissenschaften doi:10.1553/giscience2021_01_s85
Abstract: Spatial data on Low-and-Middle-Income-Country (LMIC) cities, and deprived areas within cities, are often not readily available in support of local and global information needs. To address this information gap, we propose the systematic semi-automated SLUMAP framework that provides policy-relevant information on deprived urban areas in Sub-Saharan Africa (SSA), based on free open-source software (FOSS). First, we assess user needs for spatial information on deprivation (ranging from local communities to global research and policy support). Second, we show how free or low-cost image datasets can be used for mapping the location of deprived areas at the city scale and providing an overall assessment of their spatial patterns. This is implemented as a grid-based approach using machine learning and assessing the contribution of a large number of spectral and spatial features derived from open or low-cost imagery. Third, we show how higher (spatial and spectral) resolution data can provide a detailed characterization of such areas, with a GEOBIA/machine-learning workflow and deep learning techniques. We illustrate the experiments and results on the city of Nairobi (Kenya)and discuss transferability to SSA cities. Keywords: slum, earth observation, sustainability, spatial inequalities, machine learning Published Online: 2021/06/29 10:02:48 Object Identifier: 0xc1aa5576 0x003c9b5c Rights:https://creativecommons.org/licenses/by-nd/4.0/
GI_Forum publishes high quality original research across the transdisciplinary field of Geographic Information Science (GIScience). The journal provides a platform for dialogue among GI-Scientists and educators, technologists and critical thinkers in an ongoing effort to advance the field and ultimately contribute to the creation of an informed GISociety. Submissions concentrate on innovation in education, science, methodology and technologies in the spatial domain and their role towards a more just, ethical and sustainable science and society. GI_Forum implements the policy of open access publication after a double-blind peer review process through a highly international team of seasoned scientists for quality assurance. Special emphasis is put on actively supporting young scientists through formative reviews of their submissions. Only English language contributions are published.
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Verlag der Österreichischen Akademie der Wissenschaften Austrian Academy of Sciences Press
A-1011 Wien, Dr. Ignaz Seipel-Platz 2
Tel. +43-1-515 81/DW 3420, Fax +43-1-515 81/DW 3400 https://verlag.oeaw.ac.at, e-mail: verlag@oeaw.ac.at |