Jul 2026· Journal of Civil, Construction and Environmental Engineering· Vol 11, pp. 214-224· 0 citations· 15 references
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/m
2
, 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 m
2
. 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 (T
0
) 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.
Underground mining operations depend heavily on vertical shafts for access, ventilation, and ore transport, making their structural integrity and safety critical to overall mine performance. Traditional shaft inspections, though rigorous, are limited by human accessibility, environmental hazards, and subjective evaluation. This study presents the development and initial testing of a novel unmanned aerial vehicle (UAV) system designed specifically for shaft inspections in deep mining environments. The research focuses on the GG-1 shaft in Kwielice, Poland—the country’s deepest operational shaft—where challenging conditions such as high ventilation airflow, confined geometry, and absence of GNSS signals necessitated innovative solutions. A custom-built hexacopter equipped with high-resolution cameras and photogrammetric capabilities was deployed to capture detailed visual and spatial data. This article presents complementary path of UAV evolution, from concept, early development stage and results without positioning system through to the description of final results including positioning system and all six cameras until results of high-altitude flights. Results demonstrate that UAV-based inspection can deliver sufficient precision for identifying structural irregularities, documenting shaft infrastructure, and enhancing safety monitoring. The findings highlight the potential of UAV technology as a complementary tool to conventional inspections, offering improved data quality, reduced risk to personnel, and a new approach to shaft maintenance.
Wojciech Rutkowski, Jędrzej Szczepaniak, Tomasz Lipecki et al.· Remote Sensing· 0 citations
Accurate bathymetric data are essential for the design and monitoring of coastal structures, but conventional multibeam surveys are costly and often impractical in shallow or confined areas. We evaluate a single-beam echosounder (SBES, ECT400) suspended beneath an unmanned aerial vehicle (UAV) as a rapid method with low logistical requirements for bathymetric monitoring of coastal infrastructure. Fieldwork was performed in an operational dry dock that was alternately drained and filled, enabling direct geometric validation against an ultra-high-resolution photogrammetric DEM (0.55 cm GSD). The co-registered dataset comprises N = 16,137 sonar returns to depths of ≈ 8 m. The UAV-mounted SBES produced a mean depth difference of 0.15 m (SD = 0.58 m) relative to the photogrammetric reference. From these residuals we estimate a 95% Minimum Detectable Change (MDC95) of ≈ 0.5 m when changes are assessed by aggregating repeated co-located passes. These results indicate that the UAV-SBES workflow is suitable as a Tier-1 screening tool for structural-health monitoring, effective for detecting metre- to decimetre-scale changes and triaging sites for targeted high-precision follow-up, but not for micrometre/mm-scale deformation monitoring. The method’s portability and vessel-free operation make it especially useful for frequent inspections in shallow, confined coastal settings.
Bethsaide Souza-Santos, M. Arza-García, J. Ortiz-Sanz et al.· Journal of Civil Structural...· 0 citations
Accurate spatial localization of small, transient targets in low-texture aquatic environments remains a fundamental challenge in UAV-based remote sensing, where open-water surfaces often lack stable tie points, degrading exterior orientation estimation and conventional photogrammetric georeferencing. An integrated UAV framework combining DG/AAT-BA georeferencing with deep-learning-based oriented bounding box (OBB) detection was implemented for high-precision localization, validated on the Critically Endangered Yangtze finless porpoise (YFP, Neophocaena asiaeorientalis) in the Yangtze–Poyang Lake system. The georeferencing component selects direct georeferencing (DG) in open-water scenes and automated aerial triangulation with bundle adjustment (AAT-BA) in feature-rich nearshore scenes. Validation using two static verification points showed that, relative to DG, AAT-BA reduced geometric georeferencing RMSE from 2.59 to 0.62 m under straight-flight conditions and from 3.61 to 0.67 m under turning-flight conditions. For target detection, a lightweight Laplacian edge-enhancement convolution module (LapConv) was incorporated into YOLO-OBB backbones, amplifying weak-edge and low-contrast features of partially submerged targets. Across four representative YOLO-OBB models and three group-constrained partitions, LapConv consistently improved the mean mAP@0.5, with gains of 0.026, 0.024, 0.019, and 0.026 for YOLOv8, YOLO11, YOLO12, and YOLO26, respectively. Applying this framework to six UAV missions across three ecologically and hydrologically distinct subregions enabled georeferenced mapping of porpoise distributions and visualized spatial distribution characteristics during the survey period. The approach is reproducible, minimally invasive, and potentially transferable to UAV-based monitoring of other small aquatic wildlife, providing a methodological basis for fine-scale spatial surveys and subsequent habitat analysis.
Dongxu Yang, Wanbing Ren, Yanren Li et al.· Drones· 0 citations
Unmanned Aerial Vehicle (UAV) photogrammetry is increasingly used for local-scale mapping because it enables rapid generation of high-resolution orthophotos and elevation products. However, the positional and vertical quality of these products depends strongly on the georeferencing strategy, particularly the use of Ground Control Points (GCPs). This study evaluates the effect of GCPs on the accuracy of UAV-derived orthophoto, Digital Surface Model (DSM), and Digital Terrain Model (DTM) at the premises of the Land Management Training Center (LMTC), Dhulikhel, Nepal. The same UAV image block of 334 images was processed under two workflows: one using only onboard image geolocation and another using five surveyed GCPs. Eight independent checkpoints were used for accuracy assessment. Orthophoto planimetric accuracy was evaluated using checkpoint coordinates, while vertical agreement of DSM and DTM was assessed using surveyed ground elevations. In addition, pairwise checkpoint-distance analysis and supplementary object-based comparison were used to examine relative geometric differences in the orthophotos. The with-GCP orthophoto achieved a horizontal RMSEH of 0.063 m, whereas the without-GCP orthophoto showed an RMSEH of 3.507 m. Similarly, the with-GCP DSM and DTM achieved RMSEZ values of 0.131 m and 0.143 m, respectively, compared with 15.385 m and 15.347 m in the without-GCP workflow. The without-GCP orthophoto also exhibited systematic westward and northward displacement and minor scale-related geometric differences. The results demonstrate that GCP-based processing remains essential for reliable campus-scale UAV photogrammetry when the outputs are intended for measurement, terrain representation, planning, and other applications requiring dependable absolute accuracy.
B. Bisht, Nabraj Subedi, Ram Kumar Sapkota et al.· Journal of Land Management a...· 0 citations
Abstract. Mapping at the air–water interface in shallow coastal environments remains challenging due to the need to integrate heterogeneous datasets acquired under different geometric and operational conditions. This study presents a modular uncrewed surface vehicle (USV)-based system for simultaneous above- and underwater photogrammetric surveying supported by differential GNSS positioning. The system integrates a rigid multi-camera configuration, GNSS time synchronization, and a direct georeferencing workflow based on trajectory interpolation and lever-arm calibration. Experimental results from a rocky coastal site in Sardinia (Italy) show that underwater photogrammetry can achieve centimetric absolute accuracy (2–4 cm horizontally and ~8 cm vertically) without underwater ground control points. The USV enables controlled and repeatable acquisition in very shallow environments, while UAV photogrammetry complements the reconstruction of the emerged area. Limitations related to image quality and refraction effects are discussed. The system represents a flexible and scalable solution for integrated coastal mapping and monitoring.
Sergey Khokhlov, F. Menna, E. Nocerino· The International Archives o...· 0 citations
Abstract. High-resolution monitoring of road infrastructure is essential for the early detection of geomorphological instabilities such as landslides and erosion. This study evaluates the performance of handled MMS under different vehicle-mounted configurations: a 2-meter survey pole versus a suction-cup mount, and varying acquisition speeds (10 and 20 km/h). Furthermore, a GNSS-denied scenario was simulated to test the robustness of SLAM-based processing. Initial results revealed significant geometric discrepancies (double-points artifacts and drift), particularly in the SLAM-only and high-speed datasets. To address this, an automated segment-based refinement workflow was developed using a ICP algorithm. The refinement successfully reduced the standard deviation to the level of the point cloud´s mean point spacing (5 cm). Comparative multitemporal analysis against UAV-LiDAR reference data confirms that the proposed refinement renders even SLAM-processed data viable for detecting centimetric terrain displacements. The findings demonstrate that while suction-cup mounting at 10 km/h is optimal, algorithmic refinement allows for reliable road slopes monitoring and change detection across all tested configurations.
J. M. Gómez-López, José Luis Pérez-García, A. Mozas-Calvache et al.· The International Archives o...· 0 citations
