In high-growth cities (mainly in Asia and Africa), it becomes a major challenge for urban planners to keep track of settlements and infrastructure need.
Urban planners obtain information of settlements by satellite images and in-situmeasurements. Analysing changes in satellite images needs to be done manually and is cumbersome. Collecting in-situ measurements is time consuming, costly and is often outdated in a few weeks.
Computer Vision algorithms can derive patterns from satellite data (radar and optical), perform object detection, image segmentation to detect and classify changes in urban settlements in near-real time.
Urban planners receive automatic alerts on changes and their classification. Based on this information and the development of changes in urban settlement, urban planners decide on their investments in public infrastructure such as sanitation, streets, hospitals, schools and public transportation.