Field Work’s Optimization for the Digital Capture of Large University Campuses, Combining Various Techniques of Massive Point Capture
dc.contributor.author | Pérez Martínez, José Javier | |
dc.contributor.author | Pérez Martínez, José Javier | |
dc.contributor.author | Casado Rezola, Amaia | |
dc.contributor.author | León Cascante, Iñigo | |
dc.date.accessioned | 2022-03-31T11:44:50Z | |
dc.date.available | 2022-03-31T11:44:50Z | |
dc.date.issued | 2022-03-18 | |
dc.identifier.citation | Buildings 12(3) : (2022) // Article ID 380 | es_ES |
dc.identifier.issn | 2075-5309, | |
dc.identifier.uri | http://hdl.handle.net/10810/56152 | |
dc.description.abstract | The aim of the study is to obtain fast digitalization of large urban settings. The data of two university campuses in two cities in northern Spain was captured. Challenges were imposed by the lockdown situation caused by the COVID-19 pandemic, which limited mobility and affected the field work for data readings. The idea was to significantly reduce time spent in the field, using a number of resources, and increasing efficiency as economically as possible. The research design is based on the Design Science Research (DSR) concept as a methodological approach to design the solutions generated by means of 3D models. The digitalization of the campuses is based on the analysis, evolution and optimization of LiDAR ALS points clouds captured by government bodies, which are open access and free. Additional TLS capture techniques were used to complement the clouds, with the study of support of UAV-assisted automated photogrammetric techniques. The results show that with points clouds overlapped with 360 images, produced with a combination of resources and techniques, it was possible to reduce the on-site working time by more than two thirds. | es_ES |
dc.description.sponsorship | This research was funded by the Nouvelle-Aquitaine/Euskadi/Navarre Euro-region (AECT). Project co-financed through the second session of the 2019 AECT call for projects. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | MDPI | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | |
dc.subject | LIDAR | es_ES |
dc.subject | TLS | es_ES |
dc.subject | UAV | es_ES |
dc.subject | point cloud | es_ES |
dc.subject | 3D modelling | es_ES |
dc.title | Field Work’s Optimization for the Digital Capture of Large University Campuses, Combining Various Techniques of Massive Point Capture | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.date.updated | 2022-03-24T14:47:00Z | |
dc.rights.holder | 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | es_ES |
dc.relation.publisherversion | https://www.mdpi.com/2075-5309/12/3/380/htm | es_ES |
dc.identifier.doi | 10.3390/buildings12030380 | |
dc.departamentoes | Arquitectura | |
dc.departamentoeu | Arkitektura |
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Except where otherwise noted, this item's license is described as 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).