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|Title:||Towards a Classification Scheme for Analysis of Settlement in Africa by VHR satellite imagery|
|Authors:||HALKIA Stamatia; EHRLICH Daniele; PESARESI Martino|
|Citation:||Proceedings of the 33rd International Symposium on Remote Sensing of Environment, Sustaining the Millennium Development Goals - ISBN: 978-0-932913-13-5 vol. I, II p. 653-656|
|Publisher:||International Center for Remote Sensing of Environment (ICRSE)|
|Type:||Articles in periodicals and books|
|Abstract:||The paper addresses the analysis of urban and rural settlement patterns in North, West and South Africa. The aim is to build an urban/rural classification scheme to be used in the analysis of remote sensing for risk assessments, urban and rural development applications and to support crisis management and humanitarian and disaster preparedness operations. The urban/rural classification scheme will provide the context for computer assisted image analysis aiming to automatically extract urban features from satellite imagery. Examples from urban, peri-urban, semi-rural and rural settlements have been comparatively studied in the light of regional/climatic differences, socio-political context and historical background. Although the different expressions of urban and rural patterns, and gradients thereof across Africa, do not allow for easy generalizations, it might be possible to arrive at a basic classification scheme. A basic classification scheme would be based on values of relative density at different urban/regional scales. This method of organizing settlement could provide the least common denominator between settlements varying greatly in socio-cultural content. The paper also addresses the analysis of socio-cultural markers of urban/built-up patterns into meaningful elements for remote sensing analysis. These markers point to implicit assumptions that would be useful in the risk assessments, development applications, crisis management and in the study of humanitarian crises,. Namely, presence of cars, green space, tarmac roads, all automatically detected in remote sensing, can give information about density of urban/rural patterns and the socio-cultural content of settlements. This information, can then be used effectively in a number of applications as listed above.|
|JRC Institute:||Space, Security and Migration|
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