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Analysis of institutional authors

Sanchez-Aparicio, Luis JavierAuthorVillanueva-Llaurado, PaulaAuthorAira-Zunzunegui, Jose RamonCorresponding Author

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July 31, 2025
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Article

A Holistic Solution for Supporting the Diagnosis of Historic Constructions from 3D Point Clouds

Publicated to: Remote Sensing. 17 (12): 2018- - 2025-06-11 17(12), DOI: 10.3390/rs17122018

Authors:

Sanchez-Aparicio, Luis Javier; Santamaria-Maestro, Ruben; Sanz-Honrado, Pablo; Villanueva-Llaurado, Paula; Aira-Zunzunegui, Jose Ramon; Gonzalez-Aguilera, Diego
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Affiliations

CSIC, Inst Phys & Informat Technol Leonardo Torres Queve, C Serrano 144, Madrid 28006, Spain - Author
Univ Politecn Madrid, Escuela Tecn Super Arquitectura ETSAM, Dept Bldg Struct & Phys, Avda Juan Herrera 4, Madrid 28040, Spain - Author
Univ Politecn Madrid, Escuela Tecn Super Arquitectura Madrid ETSAM, Dept Construct & Technol Architecture DCTA, Ave Juan Herrera 4, Madrid 28040, Spain - Author
Univ Salamanca, Escuela Politecn Super Avila, Dept Cartog & Land Engn, Hornos Caleros 50, Avila 05003, Spain - Author
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Abstract

This paper presents Segmentation for Diagnose (Seg4D), a holistic tool for processing 3D point clouds in the field of historical constructions. This tool incorporates state-of-the-art algorithms for the segmentation and analysis of construction systems and damage. Seg4D applies both supervised and unsupervised machine learning and deep learning methods, including the Point Transformer Neural Network for point cloud segmentation. Additionally, it facilitates the extraction of geometrical and statistical features, colour-scale conversion, noise reduction with anisotropic filters and the use of custom scripts for analysing deflections in slabs or out-of-plane movements in arches and vaults, among others. The Seg4D installer and source code are are publicly available in a GitHub repository.
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Keywords

3d point cloud3d point cloudsArtificial intelligenceCloudcomparCloudcompareConstruction damagesConstruction systemsCultural heritageCultural heritagesDeep neural networksDiagnosisHeritageHistoric preservationHistorical constructionHistorical constructionsLearning methodsLearning systemsNoise abatementReconstructioState-of-the-art algorithmsSupervised machine learningThree dimensional computer graphicsUnsupervised learningUnsupervised machine learning

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Remote Sensing due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2025, it was in position 47/258, thus managing to position itself as a Q1 (Primer Cuartil), in the category Geosciences, Multidisciplinary.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2026-04-27:

  • WoS: 3
  • Scopus: 4
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Impact and social visibility

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
  • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://oa.upm.es/94167/

As a result of the publication of the work in the institutional repository, statistical usage data has been obtained that reflects its impact. In terms of dissemination, we can state that, as of

  • Views: 16
  • Downloads: 8
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Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (SANCHEZ APARICIO, LUIS JAVIER) .

the author responsible for correspondence tasks has been AIRA ZUNZUNEGUI, JOSE RAMON.

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Awards linked to the item

This work has been supported by the Community of Madrid and the Higher Polytechnic School of Madrid through the Project CAREEN (desarrollo de nuevos metodos basados en inteligenCia ARtificial para la caracterizacion de danos en construccionEs historicas a traves de nubEs de puNtos 3D) with reference APOYO-JOVENES-21-RCDT1L-85-SL9E1R. Pablo Sanz's pre-doctoral contract is part of grant PID2022-140071OB-C21, funded by MCIN/AEI/10.13039/501100011033 and ESF+.
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