Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Jochem WC

PROVIDER: S-EPMC5863080 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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

Jochem Warren C WC Bird Tomas J TJ Tatem Andrew J AJ

Computers, environment and urban systems 20180501

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 v ...[more]

PMID: 29725149

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Dataset Information

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

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

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