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Decoding Mexico City: An Unsupervised Machine Learning Approach to Urban Fabric Segmentation Adriana, MORENOAbstract In the everevolving sprawl of Mexico City, understanding its unique urban fabric is crucial for targeted infrastructural improvements and effective regulatory interventions. This research employed cadastral data, aerial imagery, and clustering algorithms, such as Kmeans, SOMs (Kohonen Map), and HDBSCAN, to address this challenge. This multifaceted approach successfully segmented
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