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Title: Using deep learning and Open Street Maps to find features in aerial images
Author: Beltrán Segarra, Marc
Companys Rufián, Albert
Director/Tutor: Seguí Mesquida, Santi
Vitrià i Marca, Jordi
Keywords: Aprenentatge automàtic
Serveis de geolocalització
Treballs de fi de màster
Fotografia aèria
Processament digital d'imatges
Xarxes neuronals (Informàtica)
Machine learning
Location-based services
Master's thesis
Aerial photography
Digital image processing
Neural networks (Computer science)
Issue Date: 3-Jul-2018
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.
Note: 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
Appears in Collections:Màster Oficial - Fonaments de la Ciència de Dades
Programari - Treballs de l'alumnat

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