UAV-Based Photogrammetric Inspection of an Urban Bridge in a Lagoonal Environment: Case Study of the Old Cotonou Bridge

Kora Farid Carlos Yarou; Valery Kouandete Doko; Boris Ganmavo; Thede Agbelele; Mohamed Gibigaye

doi:doi:10.11648/j.jccee.20261104.16

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| Peer-Reviewed

UAV-Based Photogrammetric Inspection of an Urban Bridge in a Lagoonal Environment: Case Study of the Old Cotonou Bridge

Kora Farid Carlos Yarou^*

, Valery Kouandete Doko, Boris Ganmavo

, Thede Agbelele

, Mohamed Gibigaye

Published in Journal of Civil, Construction and Environmental Engineering (Volume 11, Issue 4)

Received: 30 June 2026 Accepted: 13 July 2026 Published: 28 July 2026

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Abstract

Bridge infrastructure in sub-Saharan Africa is often monitored with limited resources, leaving many ageing structures without a reliable geometric baseline for tracking deterioration. This paper reports on a UAV photogrammetric inspection campaign conducted on the Old Cotonou Bridge, a two-lane reinforced concrete structure crossing the coastal lagoon of Cotonou (Benin), with the aim of establishing a quantitative geometric reference for deck deformation monitoring. A flight of 573 images was captured at 56.1 m altitude using a DJI Mavic 2 Pro equipped with a Hasselblad L1D-20c 20 Mpx sensor (GSD: 1.28 cm/px), and the dataset was processed with Agisoft Metashape Professional 2.3.1 following a Structure-from-Motion and Multi-View Stereo workflow. Processing yielded a dense point cloud of 26.8 million points at 383 pts/m², a DEM at 5.11 cm/px, and a georeferenced orthomosaic in WGS 84 / UTM zone 31N; six thematic classes were identified by automatic classification, followed by manual verification of the Road and Building classes. Deck deformation was then quantified through 2D polynomial regression of the deck surface, revealing seven statistically significant depression zones (D1–D7) with amplitudes ranging from −17.3 cm to −79.4 cm relative to the reference surface, over areas of 2 to 30 m². The vertical accuracy achieved (RMSE Z = 0.44 cm) confirms that UAV photogrammetry can reliably serve as a quantitative tool for structural deformation detection on bridge decks, despite the use of only three Ground Control Points. The geometric reference dataset (T₀) produced here places at the disposal of asset managers a georeferenced database that is immediately usable for prioritising maintenance interventions on this and comparable structures.

Published in	Journal of Civil, Construction and Environmental Engineering (Volume 11, Issue 4)
DOI	10.11648/j.jccee.20261104.16
Page(s)	214-224
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2026. Published by Science Publishing Group

Keywords

Unmanned Aerial Vehicle Photogrammetry, Dense Point Cloud, Deformation Detection, Bridge Inspection, Light Detection and Ranging Classification, Orthomosaic, Structure-from-Motion

References

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APA Style

Yarou, K. F. C., Doko, V. K., Ganmavo, B., Agbelele, T., Gibigaye, M. (2026). UAV-Based Photogrammetric Inspection of an Urban Bridge in a Lagoonal Environment: Case Study of the Old Cotonou Bridge. Journal of Civil, Construction and Environmental Engineering, 11(4), 214-224. https://doi.org/10.11648/j.jccee.20261104.16

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ACS Style

Yarou, K. F. C.; Doko, V. K.; Ganmavo, B.; Agbelele, T.; Gibigaye, M. UAV-Based Photogrammetric Inspection of an Urban Bridge in a Lagoonal Environment: Case Study of the Old Cotonou Bridge. J. Civ. Constr. Environ. Eng. 2026, 11(4), 214-224. doi: 10.11648/j.jccee.20261104.16

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AMA Style

Yarou KFC, Doko VK, Ganmavo B, Agbelele T, Gibigaye M. UAV-Based Photogrammetric Inspection of an Urban Bridge in a Lagoonal Environment: Case Study of the Old Cotonou Bridge. J Civ Constr Environ Eng. 2026;11(4):214-224. doi: 10.11648/j.jccee.20261104.16

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@article{10.11648/j.jccee.20261104.16,
  author = {Kora Farid Carlos Yarou and Valery Kouandete Doko and Boris Ganmavo and Thede Agbelele and Mohamed Gibigaye},
  title = {UAV-Based Photogrammetric Inspection of an Urban Bridge in a Lagoonal Environment: Case Study of the Old Cotonou Bridge},
  journal = {Journal of Civil, Construction and Environmental Engineering},
  volume = {11},
  number = {4},
  pages = {214-224},
  doi = {10.11648/j.jccee.20261104.16},
  url = {https://doi.org/10.11648/j.jccee.20261104.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jccee.20261104.16},
  abstract = {Bridge infrastructure in sub-Saharan Africa is often monitored with limited resources, leaving many ageing structures without a reliable geometric baseline for tracking deterioration. This paper reports on a UAV photogrammetric inspection campaign conducted on the Old Cotonou Bridge, a two-lane reinforced concrete structure crossing the coastal lagoon of Cotonou (Benin), with the aim of establishing a quantitative geometric reference for deck deformation monitoring. A flight of 573 images was captured at 56.1 m altitude using a DJI Mavic 2 Pro equipped with a Hasselblad L1D-20c 20 Mpx sensor (GSD: 1.28 cm/px), and the dataset was processed with Agisoft Metashape Professional 2.3.1 following a Structure-from-Motion and Multi-View Stereo workflow. Processing yielded a dense point cloud of 26.8 million points at 383 pts/m2, a DEM at 5.11 cm/px, and a georeferenced orthomosaic in WGS 84 / UTM zone 31N; six thematic classes were identified by automatic classification, followed by manual verification of the Road and Building classes. Deck deformation was then quantified through 2D polynomial regression of the deck surface, revealing seven statistically significant depression zones (D1–D7) with amplitudes ranging from −17.3 cm to −79.4 cm relative to the reference surface, over areas of 2 to 30 m2. The vertical accuracy achieved (RMSE Z = 0.44 cm) confirms that UAV photogrammetry can reliably serve as a quantitative tool for structural deformation detection on bridge decks, despite the use of only three Ground Control Points. The geometric reference dataset (T0) produced here places at the disposal of asset managers a georeferenced database that is immediately usable for prioritising maintenance interventions on this and comparable structures.},
 year = {2026}
}

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TY  - JOUR
T1  - UAV-Based Photogrammetric Inspection of an Urban Bridge in a Lagoonal Environment: Case Study of the Old Cotonou Bridge
AU  - Kora Farid Carlos Yarou
AU  - Valery Kouandete Doko
AU  - Boris Ganmavo
AU  - Thede Agbelele
AU  - Mohamed Gibigaye
Y1  - 2026/07/28
PY  - 2026
N1  - https://doi.org/10.11648/j.jccee.20261104.16
DO  - 10.11648/j.jccee.20261104.16
T2  - Journal of Civil, Construction and Environmental Engineering
JF  - Journal of Civil, Construction and Environmental Engineering
JO  - Journal of Civil, Construction and Environmental Engineering
SP  - 214
EP  - 224
PB  - Science Publishing Group
SN  - 2637-3890
UR  - https://doi.org/10.11648/j.jccee.20261104.16
AB  - Bridge infrastructure in sub-Saharan Africa is often monitored with limited resources, leaving many ageing structures without a reliable geometric baseline for tracking deterioration. This paper reports on a UAV photogrammetric inspection campaign conducted on the Old Cotonou Bridge, a two-lane reinforced concrete structure crossing the coastal lagoon of Cotonou (Benin), with the aim of establishing a quantitative geometric reference for deck deformation monitoring. A flight of 573 images was captured at 56.1 m altitude using a DJI Mavic 2 Pro equipped with a Hasselblad L1D-20c 20 Mpx sensor (GSD: 1.28 cm/px), and the dataset was processed with Agisoft Metashape Professional 2.3.1 following a Structure-from-Motion and Multi-View Stereo workflow. Processing yielded a dense point cloud of 26.8 million points at 383 pts/m2, a DEM at 5.11 cm/px, and a georeferenced orthomosaic in WGS 84 / UTM zone 31N; six thematic classes were identified by automatic classification, followed by manual verification of the Road and Building classes. Deck deformation was then quantified through 2D polynomial regression of the deck surface, revealing seven statistically significant depression zones (D1–D7) with amplitudes ranging from −17.3 cm to −79.4 cm relative to the reference surface, over areas of 2 to 30 m2. The vertical accuracy achieved (RMSE Z = 0.44 cm) confirms that UAV photogrammetry can reliably serve as a quantitative tool for structural deformation detection on bridge decks, despite the use of only three Ground Control Points. The geometric reference dataset (T0) produced here places at the disposal of asset managers a georeferenced database that is immediately usable for prioritising maintenance interventions on this and comparable structures.
VL  - 11
IS  - 4
ER  -

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Author Information

Kora Farid Carlos Yarou

Civil Engineering Department, University of Abomey-Calavi, Abomey-Calavi, Benin

Contact Email

http://orcid.org/0009-0001-8893-6414
Valery Kouandete Doko

Civil Engineering Department, University of Abomey-Calavi, Abomey-Calavi, Benin

Contact Email
Boris Ganmavo

Civil Engineering Department, University of Abomey-Calavi, Abomey-Calavi, Benin

Contact Email

http://orcid.org/0009-0007-4252-8646
Thede Agbelele

Civil Engineering Department, University of Abomey-Calavi, Abomey-Calavi, Benin

Contact Email

http://orcid.org/0009-0003-7053-5166
Mohamed Gibigaye

Civil Engineering Department, University of Abomey-Calavi, Abomey-Calavi, Benin

Contact Email

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Plain Text BibTeX RIS

APA Style

Yarou, K. F. C., Doko, V. K., Ganmavo, B., Agbelele, T., Gibigaye, M. (2026). UAV-Based Photogrammetric Inspection of an Urban Bridge in a Lagoonal Environment: Case Study of the Old Cotonou Bridge. Journal of Civil, Construction and Environmental Engineering, 11(4), 214-224. https://doi.org/10.11648/j.jccee.20261104.16

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ACS Style

Yarou, K. F. C.; Doko, V. K.; Ganmavo, B.; Agbelele, T.; Gibigaye, M. UAV-Based Photogrammetric Inspection of an Urban Bridge in a Lagoonal Environment: Case Study of the Old Cotonou Bridge. J. Civ. Constr. Environ. Eng. 2026, 11(4), 214-224. doi: 10.11648/j.jccee.20261104.16

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AMA Style

Yarou KFC, Doko VK, Ganmavo B, Agbelele T, Gibigaye M. UAV-Based Photogrammetric Inspection of an Urban Bridge in a Lagoonal Environment: Case Study of the Old Cotonou Bridge. J Civ Constr Environ Eng. 2026;11(4):214-224. doi: 10.11648/j.jccee.20261104.16

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@article{10.11648/j.jccee.20261104.16,
  author = {Kora Farid Carlos Yarou and Valery Kouandete Doko and Boris Ganmavo and Thede Agbelele and Mohamed Gibigaye},
  title = {UAV-Based Photogrammetric Inspection of an Urban Bridge in a Lagoonal Environment: Case Study of the Old Cotonou Bridge},
  journal = {Journal of Civil, Construction and Environmental Engineering},
  volume = {11},
  number = {4},
  pages = {214-224},
  doi = {10.11648/j.jccee.20261104.16},
  url = {https://doi.org/10.11648/j.jccee.20261104.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jccee.20261104.16},
  abstract = {Bridge infrastructure in sub-Saharan Africa is often monitored with limited resources, leaving many ageing structures without a reliable geometric baseline for tracking deterioration. This paper reports on a UAV photogrammetric inspection campaign conducted on the Old Cotonou Bridge, a two-lane reinforced concrete structure crossing the coastal lagoon of Cotonou (Benin), with the aim of establishing a quantitative geometric reference for deck deformation monitoring. A flight of 573 images was captured at 56.1 m altitude using a DJI Mavic 2 Pro equipped with a Hasselblad L1D-20c 20 Mpx sensor (GSD: 1.28 cm/px), and the dataset was processed with Agisoft Metashape Professional 2.3.1 following a Structure-from-Motion and Multi-View Stereo workflow. Processing yielded a dense point cloud of 26.8 million points at 383 pts/m2, a DEM at 5.11 cm/px, and a georeferenced orthomosaic in WGS 84 / UTM zone 31N; six thematic classes were identified by automatic classification, followed by manual verification of the Road and Building classes. Deck deformation was then quantified through 2D polynomial regression of the deck surface, revealing seven statistically significant depression zones (D1–D7) with amplitudes ranging from −17.3 cm to −79.4 cm relative to the reference surface, over areas of 2 to 30 m2. The vertical accuracy achieved (RMSE Z = 0.44 cm) confirms that UAV photogrammetry can reliably serve as a quantitative tool for structural deformation detection on bridge decks, despite the use of only three Ground Control Points. The geometric reference dataset (T0) produced here places at the disposal of asset managers a georeferenced database that is immediately usable for prioritising maintenance interventions on this and comparable structures.},
 year = {2026}
}

Copy | Download

TY  - JOUR
T1  - UAV-Based Photogrammetric Inspection of an Urban Bridge in a Lagoonal Environment: Case Study of the Old Cotonou Bridge
AU  - Kora Farid Carlos Yarou
AU  - Valery Kouandete Doko
AU  - Boris Ganmavo
AU  - Thede Agbelele
AU  - Mohamed Gibigaye
Y1  - 2026/07/28
PY  - 2026
N1  - https://doi.org/10.11648/j.jccee.20261104.16
DO  - 10.11648/j.jccee.20261104.16
T2  - Journal of Civil, Construction and Environmental Engineering
JF  - Journal of Civil, Construction and Environmental Engineering
JO  - Journal of Civil, Construction and Environmental Engineering
SP  - 214
EP  - 224
PB  - Science Publishing Group
SN  - 2637-3890
UR  - https://doi.org/10.11648/j.jccee.20261104.16
AB  - Bridge infrastructure in sub-Saharan Africa is often monitored with limited resources, leaving many ageing structures without a reliable geometric baseline for tracking deterioration. This paper reports on a UAV photogrammetric inspection campaign conducted on the Old Cotonou Bridge, a two-lane reinforced concrete structure crossing the coastal lagoon of Cotonou (Benin), with the aim of establishing a quantitative geometric reference for deck deformation monitoring. A flight of 573 images was captured at 56.1 m altitude using a DJI Mavic 2 Pro equipped with a Hasselblad L1D-20c 20 Mpx sensor (GSD: 1.28 cm/px), and the dataset was processed with Agisoft Metashape Professional 2.3.1 following a Structure-from-Motion and Multi-View Stereo workflow. Processing yielded a dense point cloud of 26.8 million points at 383 pts/m2, a DEM at 5.11 cm/px, and a georeferenced orthomosaic in WGS 84 / UTM zone 31N; six thematic classes were identified by automatic classification, followed by manual verification of the Road and Building classes. Deck deformation was then quantified through 2D polynomial regression of the deck surface, revealing seven statistically significant depression zones (D1–D7) with amplitudes ranging from −17.3 cm to −79.4 cm relative to the reference surface, over areas of 2 to 30 m2. The vertical accuracy achieved (RMSE Z = 0.44 cm) confirms that UAV photogrammetry can reliably serve as a quantitative tool for structural deformation detection on bridge decks, despite the use of only three Ground Control Points. The geometric reference dataset (T0) produced here places at the disposal of asset managers a georeferenced database that is immediately usable for prioritising maintenance interventions on this and comparable structures.
VL  - 11
IS  - 4
ER  -

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