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Multistage strategy for ground point filtering on large-scale datasets
dc.contributor.author | Teijeiro Paredes, Diego | |
dc.contributor.author | Amor, Margarita | |
dc.contributor.author | Buján, Sandra | |
dc.contributor.author | Richter, Rico | |
dc.contributor.author | Döllner, Jürgen | |
dc.date.accessioned | 2024-09-09T17:37:20Z | |
dc.date.available | 2024-09-09T17:37:20Z | |
dc.date.issued | 2024-08 | |
dc.identifier.citation | Teijeiro Paredes, D., Amor López, M., Buján, S. et al. Multistage strategy for ground point filtering on large-scale datasets. J Supercomput (2024). https://doi.org/10.1007/s11227-024-06406-0 | es_ES |
dc.identifier.issn | 0920-8542 | |
dc.identifier.issn | 1573-0484 | |
dc.identifier.uri | http://hdl.handle.net/2183/38935 | |
dc.description | The point clouds and LiDAR datasets used in this work belong to the LiDAR-PNOA data repository, region of Navarra, and were provided by Gobierno de Navarra. Dataset license: LiDAR-PNOA-cob2 2017 CC-BY 4.0 scne.es. | es_ES |
dc.description.abstract | [Abstract]: Ground point filtering on national-level datasets is a challenge due to the presence of multiple types of landscapes. This limitation does not simply affect to individual users, but it is in particular relevant for those national institutions in charge of providing national-level Light Detection and Ranging (LiDAR) point clouds. Each type of landscape is typically better filtered by different filtering algorithms or parameters; therefore, in order to get the best quality classification, the LiDAR point cloud should be divided by the landscape before running the filtering algorithms. Despite the fact that the manual segmentation and identification of the landscapes can be very time intensive, only few studies have addressed this issue. In this work, we present a multistage approach to automate the identification of the type of landscape using several metrics extracted from the LiDAR point cloud, matching the best filtering algorithms in each type of landscape. An additional contribution is presented, a parallel implementation for distributed memory systems, using Apache Spark, that can achieve up to 34 x of speedup using 12 compute nodes. | es_ES |
dc.description.sponsorship | We wish to acknowledge the support received from the Centro de Investigación de Galicia ”CITIC”, funded by Xunta de Galicia and the European Union (European Regional Development Fund- Galicia 2014–2020 Program), by grant ED431G 2019/01. This research was funded by the Ministry of Science and Innovation of Spain (PID2019-104184RB-I00/AEI/10.13039/501100011033), by the Galician Government under the Consolidation Program of Competitive Research Units (Ref. ED431C 2021/30) and by grant PID2022-136435NB-I00, funded by MCIN/AEI/ 10.13039/501100011033 and by "ERDF A way of making Europe", EU. Diego Teijeiro received financial support from Xunta de Galicia and the European Social Fund (ESF) of the European Union (predoctoral fellowship ref. ED481A-2019/231). Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G 2019/01 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C 2021/30 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED481A-2019/231 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Springer | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-104184RB-I00/ES/DESAFIOS ACTUALES EN HPC: ARQUITECTURAS, SOFTWARE Y APLICACIONES | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2022-136435NB-I00/ES/ARQUITECTURAS, FRAMEWORKS Y APLICACIONES DE LA COMPUTACION DE ALTAS PRESTACIONES | es_ES |
dc.relation.uri | https://doi.org/10.1007/s11227-024-06406-0 | es_ES |
dc.rights | Atribución 4.0 Internacional | es_ES |
dc.rights | © 2024, The Authors | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | LiDAR point clouds | es_ES |
dc.subject | Landscape identification | es_ES |
dc.subject | Ground filtering | es_ES |
dc.subject | Apache spark | es_ES |
dc.title | Multistage strategy for ground point filtering on large-scale datasets | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | The Journal of Supercomputing | es_ES |
UDC.startPage | 1 | es_ES |
UDC.endPage | 28 | es_ES |
dc.identifier.doi | 10.1007/s11227-024-06406-0 |
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