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.

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.

Authors Warren C Jochem, Douglas R Leasure, Oliver Pannell, Heather R Chamberlain, Patricia Jones, Andrew J Tatem
Source Environment and Planning B
Published 01 May 2020
Full publication

More publications

COVID-19: Supporting the Government of Namibia with mobility data

Anonymised and aggregated data from Mobile Network Operators is a key data source for understanding the mobility patterns of populations, and improving decision-making and scenario planning during the COVID-19 epidemic. This data can be analysed in near real-time and provide an overview of mobility patterns across all of Namibia

GRID3 Mapping for Health – Brochure

Supported by Gavi through its INFUSE initiative, GRID3 Mapping for Health in the Democratic Republic of the Congo (DRC) is a Ministry of Health initiative, delivered in partnership with Flowminder and the Center for International Earth Science Information Network at […]

Tools for mapping multi-scale settlement patterns of building footprints: An introduction to the R package foot

Spatial datasets of building footprint polygons are becoming more widely available and accessible for many areas in the world. These datasets are important inputs for a range of different analyses, such as understanding the development of cities, identifying areas at risk of disasters, and mapping the distribution of populations. The growth of high spatial resolution imagery and computing power is enabling automated procedures to extract and map building footprints for whole countries.