Optimising 3D point cloud semantic segmentation: ML and manual refinement in the UNESCO Saltaire Village

Authors

Daniel Neagu (ORCID: https://orcid.org/0000-0002-7038-106X)
Victoria Andrea Cotella (ORCID: https://orcid.org/0000-0002-3160-7601)
Tom Sparrow (ORCID: https://orcid.org/0000-0002-0859-8823)
A. S. Wilson (ORCID: https://orcid.org/0000-0003-1346-937X)
Shamim Akbari Bavani
Matthieu Trichard
Félix Horcholle
Rémy Sangoï
Clément Lacalle
Achille Jeanvoine

Institutions

University of Bradford (GB)
École Nationale Supérieure d'Hydraulique (DZ)

Publication Details

Journal: Building Research & Information
Published: 2025-08-26
DOI: https://doi.org/10.1080/09613218.2025.2521751
Primary Topic: 3D Surveying and Cultural Heritage
Type: article
Field-Weighted Citation Impact: 0.00

Funders

Arts and Humanities Research Council

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article

Optimising 3D point cloud semantic segmentation: ML and manual refinement in the UNESCO Saltaire Village

Daniel Neagu, Victoria Andrea Cotella, Tom Sparrow, A. S. Wilson et al.

Aug 26, 2025

1 links

Building Research & Information

3D Surveying and Cultural Heritage

article

Optimising 3D point cloud semantic segmentation: ML and manual refinement in the UNESCO Saltaire Village

Daniel Neagu, Victoria Andrea Cotella, Tom Sparrow, A. S. Wilson, Shamim Akbari Bavani, Matthieu Trichard, Félix Horcholle, Rémy Sangoï, Clément Lacalle, Achille Jeanvoine

August 26, 2025article en

Abstract

No abstract available for this paper.

Building Research & InformationVol. 53(7)

University of Bradford (GB), École Nationale Supérieure d'Hydraulique (DZ)

Arts and Humanities Research Council

Openalex Percentile: Top 69%

3D Surveying and Cultural Heritage

DOI Open Access PDF OpenAlex

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