Abstract. Cities around the world are facing tremendous pressure due to rapid urbanization. They are being extended haphazardly especially in developing countries, putting strain on already depleting natural recourses. The land-use conversion to built-up areas harms the urban environment significantly. The most immediate implications of this land-use/land cover (LULC) conversion are the transition of Land Surface Temperature (LST) and the creation of Urban Heat Islands. This research investigates the spatial distribution of LST and LULC and their interrelation using satellite images from Landsat 5 (TM) and 8 (OLI/TRS) for the years 1998, 2010, and 2021. The built-up area in Lahore has grown enormously over the last two decades. Our results indicate that each year 1.26% of the land is being transformed into built-up area. Consequently, the prevailing urban development trends have also influenced the LST. In particular, we observed an average upsurge of 0.47°C per year between 1998 and 2021. If our cities continue to expanqd in the same manner, this would have serious ecological implications in the future. Thus, urban planners and policymakers need to incorporate climate-adaptive design at the community and building levels to improve the situation.
Abstract. The e-commerce industry has seen significant growth over the past few years. One significant issue that has sprung up as a result of this growth is unstructured addresses during last mile delivery. These ambiguous addresses are an established issue, particularly in developing countries like Pakistan. They are difficult to read and locate by last mile delivery riders thereby increasing delivery times and cost, negatively impacting the business of the company. Increased delivery times are also detrimental to the environment. In this paper, we aim to quantify the effects of unstructured addresses on last mile logistics. Many attempts have been made to standardise addresses to tackle this problem. Deep learning based approaches using recurrent neural networks (RNN) as well as probabilistic approaches using hidden Markov models (HMM) have been used. However, the main downside to these approaches are the underlying variation in address schemes in housing societies. We present an end to end rule based pipeline using Levenshtein distance (LD) and regular expressions (RegEx) rules which takes those unstructured addresses and outputs their structured forms along with their Geo-coordinates. The pipeline also returns the optimized route to minimize the last mile distance traveled.
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