The University of Southampton
University of Southampton Institutional Repository

Classifying settlement types from multi-scale spatial patterns of building footprints

Classifying settlement types from multi-scale spatial patterns of building footprints
Classifying settlement types from multi-scale spatial patterns of building footprints
Urban settlements and urbanised populations continue to grow rapidly and much of this transition is occurring in less developed countries. Remote sensing techniques are now often applied to monitor urbanisation and changes in settlement patterns. In particular, increasing availability of very high resolution imagery (<1 m spatial resolution) and computing power is enabling complete sets of settlement data in the form of building footprints to be extracted from imagery. These settlement data provide information on the changes occurring in cities, particularly in countries which may lack other data on urbanisation. While spatially detailed, extracted building footprints typically lack other information that identify building types or can be used to differentiate intra-urban land uses or neighbourhood types. This work demonstrates an approach to classifying settlement types through multi-scale spatial patterns of urban morphology visible in building footprint data extracted from very high resolution imagery. The work uses a Gaussian mixture modelling approach to select and hierarchically merge components into clusters. The results are maps classifying settlement types on a high spatial resolution (100 m) grid. The approach is applied in Kaduna, Nigeria; Kinshasa, Democratic Republic of the Congo; and Maputo, Mozambique and demonstrates the potential of computational methods to take advantage of large spatial datasets and extract meaningful information to support monitoring of urban areas. The model-based approach produces a hierarchy of potential clustering solutions, and we suggest that this can be used in partnership with local knowledge of the context when creating settlement typologies.
Urban morphology, classification, land use, spatial analysis, urban analytics
1-19
Jochem, Warren
ef65df67-4364-4438-92e9-f93ceedb8da1
Leasure, Douglas
c025de11-3c61-45b0-9b19-68d1d37959cd
Pannell, Oliver
370b302f-0b96-4fa5-b96b-5330cfef2263
Chamberlain, Heather
cb939de7-ac47-440e-aeb8-a2e36c110785
Jones, Patricia
d88b8e7c-e122-4231-80bb-4135e235cd41
Tatem, Andrew
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Jochem, Warren
ef65df67-4364-4438-92e9-f93ceedb8da1
Leasure, Douglas
c025de11-3c61-45b0-9b19-68d1d37959cd
Pannell, Oliver
370b302f-0b96-4fa5-b96b-5330cfef2263
Chamberlain, Heather
cb939de7-ac47-440e-aeb8-a2e36c110785
Jones, Patricia
d88b8e7c-e122-4231-80bb-4135e235cd41
Tatem, Andrew
6c6de104-a5f9-46e0-bb93-a1a7c980513e

Jochem, Warren, Leasure, Douglas, Pannell, Oliver, Chamberlain, Heather, Jones, Patricia and Tatem, Andrew (2020) Classifying settlement types from multi-scale spatial patterns of building footprints. Environment and Planning B, 1-19. (doi:10.1177/2399808320921208).

Record type: Article

Abstract

Urban settlements and urbanised populations continue to grow rapidly and much of this transition is occurring in less developed countries. Remote sensing techniques are now often applied to monitor urbanisation and changes in settlement patterns. In particular, increasing availability of very high resolution imagery (<1 m spatial resolution) and computing power is enabling complete sets of settlement data in the form of building footprints to be extracted from imagery. These settlement data provide information on the changes occurring in cities, particularly in countries which may lack other data on urbanisation. While spatially detailed, extracted building footprints typically lack other information that identify building types or can be used to differentiate intra-urban land uses or neighbourhood types. This work demonstrates an approach to classifying settlement types through multi-scale spatial patterns of urban morphology visible in building footprint data extracted from very high resolution imagery. The work uses a Gaussian mixture modelling approach to select and hierarchically merge components into clusters. The results are maps classifying settlement types on a high spatial resolution (100 m) grid. The approach is applied in Kaduna, Nigeria; Kinshasa, Democratic Republic of the Congo; and Maputo, Mozambique and demonstrates the potential of computational methods to take advantage of large spatial datasets and extract meaningful information to support monitoring of urban areas. The model-based approach produces a hierarchy of potential clustering solutions, and we suggest that this can be used in partnership with local knowledge of the context when creating settlement typologies.

Text
2399808320921208 - Version of Record
Available under License Creative Commons Attribution.
Download (1MB)

More information

Accepted/In Press date: 25 March 2020
e-pub ahead of print date: 1 May 2020
Keywords: Urban morphology, classification, land use, spatial analysis, urban analytics

Identifiers

Local EPrints ID: 439741
URI: http://eprints.soton.ac.uk/id/eprint/439741
PURE UUID: 333bedaf-4830-409f-9642-572a83a55516
ORCID for Oliver Pannell: ORCID iD orcid.org/0000-0003-2559-2818
ORCID for Andrew Tatem: ORCID iD orcid.org/0000-0002-7270-941X

Catalogue record

Date deposited: 01 May 2020 16:30
Last modified: 08 Oct 2020 16:36

Export record

Altmetrics

Contributors

Author: Warren Jochem
Author: Douglas Leasure
Author: Oliver Pannell ORCID iD
Author: Patricia Jones
Author: Andrew Tatem ORCID iD

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×