The paper describes an efficient workflow wherein UAV photogrammetry is combined with other 3D survey techniques (terrestrial photogrammetry, laser scanning and total station) to provide comprehensive documentation of a historical building. The output orthoimage of the tiled roof allowed high-lighting the covering damage state. The research aims to test and evaluate the feasibility of automatically mapping roof damage using an image classification procedure based on supervised machine learning. The methodology was validated on a historical building, now suffering from a serious state of neglect.

Aerial-photogrammetric survey for supervised classification and mapping of roof damages

FAUSTA FIORILLO;LUCA PERFETTI;GIULIANA CARDANI
2022-01-01

Abstract

The paper describes an efficient workflow wherein UAV photogrammetry is combined with other 3D survey techniques (terrestrial photogrammetry, laser scanning and total station) to provide comprehensive documentation of a historical building. The output orthoimage of the tiled roof allowed high-lighting the covering damage state. The research aims to test and evaluate the feasibility of automatically mapping roof damage using an image classification procedure based on supervised machine learning. The methodology was validated on a historical building, now suffering from a serious state of neglect.
D-SITE, Drones - Systems of Information on Cultural Heritage for a spatial and social investigation
978-88-6952-160-7
UAVs, Machine Learning, Image Segmentation, Built Heritage, Damage Survey.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1219988
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