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Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/133457
Using deep learning and Open Street Maps to find features in aerial images
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Abstract
[en] A great amount of the interesting information captured by aerial imagery is still not being used given how labour intensive the processing and annotation of these images is. Despite this, improvements in technology and advancements in the computer vision field have made available tools and techniques that can help make this process semi-automatized. In this project we focus on the use case of extracting roads from aerial imagery. For this purpose, we will study and compare models based on image segmentation using deep learning and RoadTracer, a revolutionary model proposed recently.
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Treballs finals del Màster de Fonaments de Ciència de Dades, Facultat de matemàtiques, Universitat de Barcelona, Any: 2018, Tutor: Santi Seguí Mesquida i Jordi Vitrià i Marca
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BELTRÁN SEGARRA, Marc and COMPANYS RUFIÁN, Albert. Using deep learning and Open Street Maps to find features in aerial images. [consulted: 10 of August of 2026]. Available at: https://hdl.handle.net/2445/133457