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Archivio digitale delle tesi discusse presso l’Università di Pisa

Tesi etd-01302022-125214


Tipo di tesi
Tesi di laurea magistrale
Autore
ZARA, GIULIANO
URN
etd-01302022-125214
Titolo
Using computer vision for detection and classification of building equipment in satellite imagery
Dipartimento
INGEGNERIA DELL'INFORMAZIONE
Corso di studi
ARTIFICIAL INTELLIGENCE AND DATA ENGINEERING
Relatori
relatore Prof. Cimino, Mario Giovanni Cosimo Antonio
correlatore Prof.ssa Vaglini, Gigliola
tutor Zitarosa, Romeo
Parole chiave
  • building detection
  • computer vision
  • deep learning
  • roof segmentation
  • satellite imagery
Data inizio appello
18/02/2022
Consultabilità
Non consultabile
Data di rilascio
18/02/2062
Riassunto
Traditional means of obtaining geo-localized information on building equipment (e.g., solar panels and other installations) are limited in availability and geo-spatial resolution and basically rely on intensive human on-site analysis. In this work, a different approach, based on computer vision algorithms and high-resolution color satellite imagery, is investigated for automating this information acquisition process. The main objectives are therefore the creation of a model that allows the automatic recognition of roofs of buildings and the creation of a model that allows the automatic recognition of equipment on roofs in satellite images. The problems that will be analyzed fall in the field of image segmentation, one of the fundamental problems in computer vision; thus some of the techniques proposed in the literature in recent years will be initially studied, in particular the U-Net and Pyramid Attention Network architectures. These architectures will then be applied and their effectiveness will be studied, comparing them with the current state of the art.
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