Population estimation mining from satellite imagery

The collection of census data is an important task with respect to providing support for decision makers. However, the collection of census data is also resource intensive. This is especially the case in areas which feature poor communication and transport networks. In this thesis a number of methods are proposed for collecting census data by applying prediction techniques to relevant satellite imagery. The test site for the work is a collection of villages lying some 300km to the northwest of Addis Ababa in Ethiopia. The idea is to build a predictor that can label households according to “family” size. To this end training data has been obtained by collecting “on ground” census data and matching this up with satellite imagery. The fundamental idea is to segment satellite images so as to obtain satellite sub-images describing individual households and representing these segmentations using a number of proposed representations: graph-based, histogram based and texture based. By pairing each represented household with the collated census data, namely family size, a predictor can be constructed to predict household sizes according to the nature of each representation. The generated predictor can then be used to provide a quick and easy mechanism for the approximate collection of census data that does not require significant resource