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Building footprint datasets are valuable for a variety of uses in urban settings. For a number of urban applications, polygonal building outlines with regularised bounds are required and are extremely challenging to prepare. We propose a deep learning strategy based on convolutional neural networks for retrieving building footprints. The model was trained using images from a variety of places across the metropolis, highlighting differences in land use patterns and the built environment. The evaluation measures indicate how the accuracy characteristics of distinct built-up settings differ. The results of the model are equivalent to cutting-edge building extraction methods. [ABSTRACT FROM AUTHOR]