UAV-Based Photogrammetric Inspection in Deep Vertical Shafts: A Case Study from the KGHM GG-1 Mineshaft in Poland

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

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.

Validation of UAV-Derived Discontinuity Data Against Field-Based Measurements for Kinematic Analysis in an Open-Pit Mine

This study conducted a kinematic analysis of rock slopes in an open-pit feldspar mine in Çine (Aydın, Türkiye) and explicitly focused on the validation of uncrewed aerial vehicle (UAV)–based photogrammetry with structural data obtained from traditional field surveys. Input parameters (dip direction and dip angles) were collected using a traditional geological compass at 119 observation points and extracted from a UAV-generated dense point cloud using the CloudCompare Compass plugin. A direct reliability assessment between the two methods revealed that the UAV-derived orientations matched the traditional field measurements with high precision, specifically within a 2° to 3° margin of error. Furthermore, the UAV approach effectively facilitated data collection in inaccessible or hazardous zones, such as high bench faces and block debris areas, safely increasing spatial data coverage and saving time. Using these validated parameters, kinematic analyses identified 65 wedge, 40 planar, and 87 toppling failures, predominantly concentrated on northwest-facing slopes. Spatial evaluations using geographic information system (GIS) mapping techniques highlighted the finding that stable wedges are restricted to paleo-stream channels, whereas unstable cases are located in weathered zones affected by water infiltration. Ultimately, the study demonstrates that integration of UAV photogrammetry with conventional surveys provides a highly reliable, safe, and efficient framework for kinematic assessments in open-pit mines.

A Strategic Framework for Integrating Unmanned Aerial Vehicles (UAV)-Based Pavement Assessments into the Egyptian Road Network

Pavement condition assessment is essential for effective road network management, as paved roads deteriorate over time due to traffic loading and environmental effects. Traditional pavement surveys rely on in-situ measurements and visual inspections to identify surface distresses such as cracking, raveling, and weathering. Although widely used, these methods are often labour-intensive, time-consuming, costly, and may disrupt traffic while exposing inspectors to safety risks. Recent advances in unmanned aerial systems (UAS) provide a promising alternative for pavement condition assessment. UAV-based surveys enable rapid data collection over large areas using high-resolution imaging and sensor technologies, which can be integrated with artificial intelligence (AI) techniques for automated pavement distress detection and analysis. In Egypt, the rapid expansion of the road network and increasing maintenance demands highlight the need for an efficient, continuous, and reliable pavement monitoring system. This study presents an Egypt-focused framework that links UAV data acquisition, AI-based distress detection, and PCI-based decision-making to support the integration of UAV-based pavement inspection into existing road management practices. This study supports an Egypt-focused framework for integrating UAV-based pavement inspection into existing road management practices. The proposed framework outlines UAV data acquisition, AI-based distress detection, and pavement condition evaluation workflows, while considering local environmental, operational, and regulatory constraints. The framework is informed by successful international applications and is intended to enable safer, faster, and more cost-effective pavement assessment to support sustainable road network management in Egypt.

Real-Time Monitoring of Mine Dumps Using UAV-Based Remote Sensing Technology and Robotic Total Station: A Review-Based Methodological Framework

Drone-Based Photogrammetry for Pavement Deterioration Detection and Quantification in Airport Infrastructure

Abstract. The maintenance of airport pavements is critical to ensuring the safety and efficiency of air operations. Conventional inspection methods are often time-consuming, subjective, and prone to inconsistencies in data collection. Recent advances in unmanned aerial vehicle (UAV) photogrammetry offer a potential alternative for improving inspection efficiency and measurement accuracy. This study evaluates the applicability of UAV-based photogrammetry for the detection and quantification of pavement distresses under conditions representative of airport infrastructure. Image data were acquired at different flight altitudes and overlap configurations and processed using Structure-from-Motion techniques to generate high-resolution orthomosaics and Digital Elevation Models (DEMs). The resulting datasets were analyzed to identify, delineate, and classify deterioration types and severity levels. The results indicate that a flight altitude of 10 m combined with 80% longitudinal and 70% transversal overlap provides an optimal balance between spatial resolution and operational efficiency. Under unobstructed conditions, photogrammetric analysis detected more than 98% of existing distresses and enabled more precise geometric delineation compared to traditional field-based methods. Undetected distresses were primarily associated with shadowed or obstructed areas, highlighting the influence of environmental conditions on detection performance. Overall, the findings demonstrate that UAV-based photogrammetry is a reliable and efficient approach for pavement condition assessment, with significant potential to enhance data quality and reduce inspection time in airport infrastructure management.

A reference-consistent UAV–total station framework for multi-temporal tilt and corrosion monitoring of slender tubular towers

Wind turbine towers in coastal environments are exposed to geometric misalignment and surface corrosion, yet these condition indicators are commonly assessed separately and without a consistent reference across inspection epochs. This study develops a reference-consistent structural health monitoring framework that integrates total-station surveying and multi-temporal unmanned aerial vehicle imagery for concurrent monitoring of view-dependent image-space inclination indicators and visible corrosion. A baseline lower-segment reference axis is established in a fixed geodetic coordinate system. The geodetically controlled baseline image configuration is transferred to subsequent epochs through covariance-weighted control-image constraints, while RTK-GNSS and inertial observations support exterior-orientation estimation. The angular difference between the projected reference axis and an image-derived visual symmetry axis defines the inclination indicator. The combined single-view standard uncertainty is 0.041°, the expanded uncertainty is 0.082°, and the engineering monitoring threshold is 0.098°. Five UAV epochs acquired between October 2024 and June 2025 yield first-to-final angular-change magnitudes of 0.030° to 0.074° across eight fixed viewing directions. These values remain below the single-view expanded-uncertainty reference magnitude and are therefore interpreted as monitoring-level observations rather than statistically confirmed structural deformation. An independent total-station re-survey at the final epoch yields validation residuals from −0.032° to +0.044°, with a mean absolute residual of 0.017° and a root-mean-square residual of 0.023°, supporting monitoring-level consistency between the UAV-derived and projected total-station changes. Visible corrosion is segmented using YOLO11-seg and normalized with respect to the valid, observable tower-surface region. The independent test set yields a precision of 79.6%, a recall of 88.0%, and an F1 score of 0.84. The visible corrosion extent index increases from 0.04 to 0.12%, corresponding to a descriptive transition from ASTM D610 grade 8 to grade 7. The framework provides a reference-consistent and uncertainty-aware basis for maintenance-oriented monitoring of slender tubular towers.