Abstract. Several Pakistani cities are among the world’s most polluted. In the previous three years, air pollution in Lahore has been considerably over World Health Organization guideline levels, endangering the lives of the city’s more than 11 million citizens. In this paper, we investigate the city’s capability to combat air pollution by analyzing three essential aspects: (1) Data, (2) Capacity, and (3) Public awareness. Several studies have reported the need for expansion of the current air quality monitoring network. In this work, we also provide a context-aware location recommendation algorithm for installing the new air quality stations in Lahore. Data from four publicly available reference-grade continuous air quality monitoring stations and nine low-cost air quality measuring equipment are also analyzed. Our findings show that in order to measure and mitigate the effects of air pollution in Lahore, there is an urgent need for capacity improvement (installation of reference-grade and low-cost air quality sensors) and public availability of reliable air quality data. We further assessed public awareness by conducting a survey. The questionnaire results showed huge gaps in public awareness about the harms of the air quality conditions. Lastly, we provided a few recommendations for designing data-driven policies for dealing with the current apocalyptic air quality situation in Lahore.
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%.
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