Document - On assisting and automatizing the semantic segmentation of masonry walls

2022

Journal article Open Access

On assisting and automatizing the semantic segmentation of masonry walls

Pavoni G., Giuliani F., De Falco A., Corsini M., Ponchio F., Callieri M., Cignoni P.

Cultural Heritage Semantic segmentation Computer Science Applications Conservation Information Systems Historical masonry Computer Graphics and Computer-Aided Design Deep Learning TAGLAB

In Architectural Heritage, the masonry's interpretation is an essential instrument for analysing the construction phases, the assessment of structural properties, and the monitoring of its state of conservation. This work is generally carried out by specialists that, based on visual observation and their knowledge, manually annotate ortho-images of the masonry generated by photogrammetric surveys. This results in vector thematic maps segmented according to their construction technique (isolating areas of homogeneous materials/structure/texture or each individual constituting block of the masonry) or state of conservation, including degradation areas and damaged parts. This time-consuming manual work, often done with tools that have not been designed for this purpose, represents a bottleneck in the documentation and management workflow and is a severely limiting factor in monitoring large-scale monuments (e.g., city walls). This article explores the potential of AI-based solutions to improve the efficiency of masonry annotation in Architectural Heritage. This experimentation aims at providing interactive tools that support and empower the current workflow, benefiting from specialists' expertise.

Source: Journal on computing and cultural heritage (Online) 15 (2022). doi:10.1145/3477400

Publisher: Association for Computing Machinery, New York, NY , Stati Uniti d'America

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@article{oai:it.cnr:prodotti:466814,
	title = {On assisting and automatizing the semantic segmentation of masonry walls},
	author = {Pavoni G. and Giuliani F. and De Falco A. and Corsini M. and Ponchio F. and Callieri M. and Cignoni P.},
	publisher = {Association for Computing Machinery, New York, NY , Stati Uniti d'America},
	doi = {10.1145/3477400},
	journal = {Journal on computing and cultural heritage (Online)},
	volume = {15},
	year = {2022}
}