The effects of the Maputo ring road on the quantity and quality of nearby housing
Using convolutional neural networks applied to satellite images covering a 25 km x 12 km rectangle on the northern outskirts of Greater Maputo, we detect and classify buildings from 2010 and 2018 in order to compare the development in quantity and quality of buildings from before and after construction of a major section of ring road.
In addition, we analyse how the effects vary by distance to the road and conclude that the area has seen large overall growth in both quantity and quality of housing, but it is not possible to distinguish growth close to the road from general urban growth.
Finally, the paper contributes methodologically to a growing strand of literature focused on combining machine-learning image recognition and the availability of high-resolution satellite images. We examine the extent to which it is possible to exploit these methods to analyse changes over time and thus provide an alternative (or complement) to traditional impact analyses.