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Flight Path Setting and Data Quality Assessments for Unmanned-Aerial-Vehicle-Based Photogrammetric Bridge Deck Documentation
Imagery from Unmanned Aerial Vehicles can be used to generate three-dimensional (3D) point cloud models. However, final data quality is impacted by the flight altitude, camera angle, overlap rate, and data processing strategies. Typically, both overview images and redundant close-range images are co...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10459964/ https://www.ncbi.nlm.nih.gov/pubmed/37631696 http://dx.doi.org/10.3390/s23167159 |
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author | Chen, Siyuan Zeng, Xiangding Laefer, Debra F. Truong-Hong, Linh Mangina, Eleni |
author_facet | Chen, Siyuan Zeng, Xiangding Laefer, Debra F. Truong-Hong, Linh Mangina, Eleni |
author_sort | Chen, Siyuan |
collection | PubMed |
description | Imagery from Unmanned Aerial Vehicles can be used to generate three-dimensional (3D) point cloud models. However, final data quality is impacted by the flight altitude, camera angle, overlap rate, and data processing strategies. Typically, both overview images and redundant close-range images are collected, which significantly increases the data collection and processing time. To investigate the relationship between input resources and output quality, a suite of seven metrics is proposed including total points, average point density, uniformity, yield rate, coverage, geometry accuracy, and time efficiency. When applied in the field to a full-scale structure, the UAV altitude and camera angle most strongly affected data density and uniformity. A 66% overlapping was needed for successful 3D reconstruction. Conducting multiple flight paths improved local geometric accuracy better than increasing the overlapping rate. The highest coverage was achieved at 77% due to the formation of semi-irregular gridded gaps between point groups as an artefact of the Structure from Motion process. No single set of flight parameters was optimal for every data collection goal. Hence, understanding flight path parameter impacts is crucial to optimal UAV data collection. |
format | Online Article Text |
id | pubmed-10459964 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-104599642023-08-27 Flight Path Setting and Data Quality Assessments for Unmanned-Aerial-Vehicle-Based Photogrammetric Bridge Deck Documentation Chen, Siyuan Zeng, Xiangding Laefer, Debra F. Truong-Hong, Linh Mangina, Eleni Sensors (Basel) Article Imagery from Unmanned Aerial Vehicles can be used to generate three-dimensional (3D) point cloud models. However, final data quality is impacted by the flight altitude, camera angle, overlap rate, and data processing strategies. Typically, both overview images and redundant close-range images are collected, which significantly increases the data collection and processing time. To investigate the relationship between input resources and output quality, a suite of seven metrics is proposed including total points, average point density, uniformity, yield rate, coverage, geometry accuracy, and time efficiency. When applied in the field to a full-scale structure, the UAV altitude and camera angle most strongly affected data density and uniformity. A 66% overlapping was needed for successful 3D reconstruction. Conducting multiple flight paths improved local geometric accuracy better than increasing the overlapping rate. The highest coverage was achieved at 77% due to the formation of semi-irregular gridded gaps between point groups as an artefact of the Structure from Motion process. No single set of flight parameters was optimal for every data collection goal. Hence, understanding flight path parameter impacts is crucial to optimal UAV data collection. MDPI 2023-08-14 /pmc/articles/PMC10459964/ /pubmed/37631696 http://dx.doi.org/10.3390/s23167159 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/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/). |
spellingShingle | Article Chen, Siyuan Zeng, Xiangding Laefer, Debra F. Truong-Hong, Linh Mangina, Eleni Flight Path Setting and Data Quality Assessments for Unmanned-Aerial-Vehicle-Based Photogrammetric Bridge Deck Documentation |
title | Flight Path Setting and Data Quality Assessments for Unmanned-Aerial-Vehicle-Based Photogrammetric Bridge Deck Documentation |
title_full | Flight Path Setting and Data Quality Assessments for Unmanned-Aerial-Vehicle-Based Photogrammetric Bridge Deck Documentation |
title_fullStr | Flight Path Setting and Data Quality Assessments for Unmanned-Aerial-Vehicle-Based Photogrammetric Bridge Deck Documentation |
title_full_unstemmed | Flight Path Setting and Data Quality Assessments for Unmanned-Aerial-Vehicle-Based Photogrammetric Bridge Deck Documentation |
title_short | Flight Path Setting and Data Quality Assessments for Unmanned-Aerial-Vehicle-Based Photogrammetric Bridge Deck Documentation |
title_sort | flight path setting and data quality assessments for unmanned-aerial-vehicle-based photogrammetric bridge deck documentation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10459964/ https://www.ncbi.nlm.nih.gov/pubmed/37631696 http://dx.doi.org/10.3390/s23167159 |
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