ESTIMATING SPATIO-TEMPORAL URBAN DEVELOPMENT USING AI
Keywords: Building Counts, Spatio-temporal profile, Digital Maps, Semantic Segmentation, Satellite Imagery, Remote Sensing, Urban data
Abstract. Estimating the spatio-temporal profile of a building’s construction using high-resolution satellite images is a critical problem since it can be utilized for a variety of data-driven urban initiatives. One strategy to achieve this is to extract building footprints and track them in multi-temporal data as observed in SpaceNet’s Challenges. Although several unique solutions have been presented for this problem, this task can become extremely difficult for partially obscured buildings with densely overlapping boundaries, such as those found in underdeveloped countries like Pakistan. Consequently, in this paper we propose a framework to address this problem by merging built-up area segmentation with digital maps. In the first step, satellite image is passed to a deep learning model that predicts segmentation masks over the built-up area following which building construction profiles are generated by overlaying digital maps over these predicted masks. We compare the results with ground truth profiles and our results show that the proposed method extracts building counts and construction profiles with an accuracy of 95%.