Please use this identifier to cite or link to this item:
https://dipositint.ub.edu/dspace/handle/2445/186070
Full metadata record
DC Field | Value | Language |
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dc.contributor.advisor | Escalera Guerrero, Sergio | - |
dc.contributor.advisor | Marín Tur, Javier | - |
dc.contributor.author | Tylson Baixauli, Emilio | - |
dc.date.accessioned | 2022-05-30T10:24:30Z | - |
dc.date.available | 2022-05-30T10:24:30Z | - |
dc.date.issued | 2021-06-30 | - |
dc.identifier.uri | http://hdl.handle.net/2445/186070 | - |
dc.description | Treballs finals del Màster de Fonaments de Ciència de Dades, Facultat de matemàtiques, Universitat de Barcelona. Curs: 2020-2021. Tutor: Sergio Escalera Guerrero i Javier Marín Tur | ca |
dc.description.abstract | [en] This work presents Satellite Style and Structure Generative Adversarial Network (SSGAN), a generative model of high resolution satellite imagery to support image segmentation. Based on spatially adaptive denormalization modules (SPADE) that modulate the activations with respect to segmentation map structure in addition to global descriptor vectors that capture the semantic information in a vector with respect to Open Street Maps (OSM) classes, this model is able to produce consistent aerial imagery. By decoupling the generation of aerial images into a structure map and a carefully defined style vector, we were able to improve the realism and geodiversity of the synthesis with respect to the state-of-the-art baseline. Therefore, the proposed model allows to control the generation not only with respect to the desired structure, but also with respect to a geographic area. | ca |
dc.format.extent | 39 p. | - |
dc.format.mimetype | application/pdf | - |
dc.language.iso | eng | ca |
dc.rights | cc-by-nc-nd (c) Emilio Tylson Baixauli, 2021 | - |
dc.rights | codi: GPL (c) Emilio Tylson Baixauli, 2021 | - |
dc.rights.uri | http://www.gnu.org/licenses/gpl-3.0.ca.html | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ | * |
dc.source | Màster Oficial - Fonaments de la Ciència de Dades | - |
dc.subject.classification | Imatges satel·litàries | - |
dc.subject.classification | Visió per ordinador | - |
dc.subject.classification | Aprenentatge automàtic | - |
dc.subject.classification | Treballs de fi de màster | - |
dc.subject.other | Remote-sensing images | - |
dc.subject.other | Computer vision | - |
dc.subject.other | Machine learning | - |
dc.subject.other | Master's theses | - |
dc.title | SSSGAN:Satellite Style and Structure Generative Adversarial Networks | ca |
dc.type | info:eu-repo/semantics/masterThesis | ca |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca |
Appears in Collections: | Programari - Treballs de l'alumnat Màster Oficial - Fonaments de la Ciència de Dades |
Files in This Item:
File | Description | Size | Format | |
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ETylson-sssgan_tfm_ub-e0beb851662f.zip | Codi font | 72.81 MB | zip | View/Open |
tfg_tylson_baixauli_emilio.pdf | Memòria | 45 MB | Adobe PDF | View/Open |
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