In-situ slum upgrading projects include infrastructural improvements such as new roads, which are perceived to improve the quality of life for the residents and encourage structural improvements at a household level. Although these physical changes are easily visible in satellite imagery, it is more difficult to track incremental improvements undertaken by the residents – which are perhaps more closely linked to the socio-economic development of the households themselves. The improved detail provided by imagery obtained from Unmanned Aerial Vehicles (UAVs) has the potential to monitor these more subtle changes in a settlement. This paper provides a framework which takes advantage of high-resolution imagery and a detailed elevation model from UAVs to detect changes in informal settlements. The proposed framework leverages expert knowledge to provide training labels for deep learning and thus avoids the cost of manual labelling. The semantic classification is then used to interpret a change mask and identify: new buildings, the creation of open spaces, and incremental roof upgrading in an informal settlement. The methodology is demonstrated on UAV imagery of an informal settlement in Kigali, Rwanda, successfully identifying changes between 2015 and 2017 with an Overall Accuracy of 95 % and correctly interpreting changes with an Overall Accuracy of 91 %. Results reveal that almost half the buildings in the settlement show visible changes in the roofing material, and 61 % of these changed less than 1m². This demonstrates the incremental nature of housing improvements in the settlement.
|Number of pages||6|
|Journal||International Journal of Applied Earth Observation and Geoinformation (JAG)|
|Early online date||3 Apr 2020|
|Publication status||Published - Aug 2020|