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Identifying residential neighbourhood types from settlement points in a machine learning approach

Remote sensing techniques are now commonly applied to map and monitor urban land uses to measure growth and to assist with development and planning. Recent work in this area has highlighted the use of textures and other spatial features that can be measured in very high spatial resolution imagery. F...

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Autores principales: Jochem, Warren C., Bird, Tomas J., Tatem, Andrew J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Pergamon 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5863080/
https://www.ncbi.nlm.nih.gov/pubmed/29725149
http://dx.doi.org/10.1016/j.compenvurbsys.2018.01.004
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author Jochem, Warren C.
Bird, Tomas J.
Tatem, Andrew J.
author_facet Jochem, Warren C.
Bird, Tomas J.
Tatem, Andrew J.
author_sort Jochem, Warren C.
collection PubMed
description Remote sensing techniques are now commonly applied to map and monitor urban land uses to measure growth and to assist with development and planning. Recent work in this area has highlighted the use of textures and other spatial features that can be measured in very high spatial resolution imagery. Far less attention has been given to using geospatial vector data (i.e. points, lines, polygons) to map land uses. This paper presents an approach to distinguish residential settlement types (regular vs. irregular) using an existing database of settlement points locating structures. Nine data features describing the density, distance, angles, and spacing of the settlement points are calculated at multiple spatial scales. These data are analysed alone and with five common remote sensing measures on elevation, slope, vegetation, and nighttime lights in a supervised machine learning approach to classify land use areas. The method was tested in seven provinces of Afghanistan (Balkh, Helmand, Herat, Kabul, Kandahar, Kunduz, Nangarhar). Overall accuracy ranged from 78% in Kandahar to 90% in Nangarhar. This research demonstrates the potential to accurately map land uses from even the simplest representation of structures.
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spelling pubmed-58630802018-05-01 Identifying residential neighbourhood types from settlement points in a machine learning approach Jochem, Warren C. Bird, Tomas J. Tatem, Andrew J. Comput Environ Urban Syst Article Remote sensing techniques are now commonly applied to map and monitor urban land uses to measure growth and to assist with development and planning. Recent work in this area has highlighted the use of textures and other spatial features that can be measured in very high spatial resolution imagery. Far less attention has been given to using geospatial vector data (i.e. points, lines, polygons) to map land uses. This paper presents an approach to distinguish residential settlement types (regular vs. irregular) using an existing database of settlement points locating structures. Nine data features describing the density, distance, angles, and spacing of the settlement points are calculated at multiple spatial scales. These data are analysed alone and with five common remote sensing measures on elevation, slope, vegetation, and nighttime lights in a supervised machine learning approach to classify land use areas. The method was tested in seven provinces of Afghanistan (Balkh, Helmand, Herat, Kabul, Kandahar, Kunduz, Nangarhar). Overall accuracy ranged from 78% in Kandahar to 90% in Nangarhar. This research demonstrates the potential to accurately map land uses from even the simplest representation of structures. Pergamon 2018-05 /pmc/articles/PMC5863080/ /pubmed/29725149 http://dx.doi.org/10.1016/j.compenvurbsys.2018.01.004 Text en © 2018 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Jochem, Warren C.
Bird, Tomas J.
Tatem, Andrew J.
Identifying residential neighbourhood types from settlement points in a machine learning approach
title Identifying residential neighbourhood types from settlement points in a machine learning approach
title_full Identifying residential neighbourhood types from settlement points in a machine learning approach
title_fullStr Identifying residential neighbourhood types from settlement points in a machine learning approach
title_full_unstemmed Identifying residential neighbourhood types from settlement points in a machine learning approach
title_short Identifying residential neighbourhood types from settlement points in a machine learning approach
title_sort identifying residential neighbourhood types from settlement points in a machine learning approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5863080/
https://www.ncbi.nlm.nih.gov/pubmed/29725149
http://dx.doi.org/10.1016/j.compenvurbsys.2018.01.004
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