Creating a Dataset for the Detection and Segmentation of Degradation Phenomena in Notre-Dame de Paris - Archive ouverte HAL
Communication Dans Un Congrès Année : 2024

Creating a Dataset for the Detection and Segmentation of Degradation Phenomena in Notre-Dame de Paris

Résumé

After the fire that destroyed most of the Notre-Dame de Paris cathedral's roof and vaults, scientists gathered in an effort to help the restoration process of the cathedral. Several digital methods and heterogeneous data acquisitions were introduced in the process, including many images and annotations. % our main focus Part of this data focuses on stone degradation phenomena, a crucial element when evaluating the damages caused by the fire and the state of the cathedral before the restoration started. In this paper, we present the first implementation of a dataset creation pipeline with the aim of training AI models to automatically detect and segment stone alteration patterns in images taken in the context of the restoration of Cultural Heritage buildings. Our resulting dataset will be improved in a near future with more data, while conforming with the ambition to provide our experts and researchers with reliable, structured data.
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Dates et versions

hal-04727818 , version 1 (09-10-2024)

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Laura Willot, Kévin Réby, Adeline Manuel, Valerie Gouet-Brunet, Dan Vodislav, et al.. Creating a Dataset for the Detection and Segmentation of Degradation Phenomena in Notre-Dame de Paris. 6th Workshop on AnalySis, Understanding and ProMotion of HeritAge Contents (SUMAC '24), ACM Multimedia 2024, Oct 2024, Melbourne (AUS), France. pp.5-12, ⟨10.1145/3689094.3689473⟩. ⟨hal-04727818⟩
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