Jul 2026· ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences· Vol XI-2-2026, pp. 631-637· 0 citations· 6 references
Abstract
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.
Abstract. Traditional pavement inspection and data collection are often constrained by traffic conditions, operational safety, and equipment costs, making it difficult to achieve both efficiency and large-scale coverage. To address these limitations, this study employs a Pavement Roughness Index and Distress Extraction System (PRIDEs), which integrates high-resolution industrial cameras, high-precision global navigation satellite system (GNSS), wheel pulse sensors, and an onboard computer to acquire high-quality images under high-speed driving conditions. Using photogrammetry and computer vision techniques, camera poses are reconstructed to generate dense point clouds, digital surface models (DSMs), and orthophotos for detailed pavement distress analysis. However, the acquired imagery is affected by dynamic shadows and lens-focusing induced blur, resulting in ghosting artifacts and inconsistent orthophoto quality. To mitigate these issues, this study proposes a masking strategy during orthophoto generation, where U-Net is employed to detect shadow regions and Laplacian variance is used to identify blurred areas. By integrating these masks, more uniform and higher-quality orthophotos can be produced. Experimental results demonstrate that the proposed approach effectively reduces false positives and false negatives of crack detection caused by shadows and blur, thereby improving the reliability of orthophotos for automated pavement condition assessment.
Yueh-Che Li, J. Jhan· The International Archives o...· 0 citations
Abstract. UAS photogrammetry has become an efficient solution for acquiring high-resolution geospatial data for urban mapping, environmental monitoring, and 3D modelling. However, mission planning still involves a trade-off between data quality and operational efficiency, particularly regarding flight altitude, which directly affects ground sample distance (GSD), point cloud density, and positional accuracy. This study evaluates the influence of flight altitude through a controlled comparison of two urban photogrammetric surveys: a low-altitude flight at 61.2 m (GSD = 1.56 cm/pix, 420 images) and a higher-altitude flight at 121 m (GSD = 3.11 cm/pix, 116 images). Both surveys used RGB cameras with equivalent image resolution mounted on different platforms, which constitutes an experimental limitation, while overlap and processing parameters were kept constant. The results show that the lower-altitude flight produced denser data and better geometric performance, with lower reprojection error and lower check point RMSE. In contrast, the higher-altitude flight provided greater operational efficiency, covering a larger area with fewer images and lower computational demand. These findings indicate that both strategies are technically viable but suited to different objectives: lower altitudes favour geometric detail and positional accuracy, whereas higher altitudes improve productivity and area coverage. Therefore, flight altitude should be selected according to project requirements, balancing geometric quality and operational efficiency.
Emanuel Amorim, Diogo Inojosa, Jailson Rodrigues Júnior et al.· The International Archives o...· 0 citations
In the aftermath of natural disasters, roadway obstructions can hinder access to impacted communities, which can severely impact emergency response and evacuation efforts. Traditional ground-based and aerial reconnaissance methods for obstruction detection are often limited by cost, accessibility, and efficiency. This study introduces a novel framework that compares postdisaster unmanned aerial vehicle (UAV) images with predisaster satellite images to detect and segment roadway obstructions and estimate remaining accessible road width to provide emergency managers with updated status of roadway networks. The approach uses the You Only Look Once version 8 (YOLOv8) algorithm to segment aerial roadways, and then these are compared with predisaster reference images at the same location to identify changes in road conditions, notably reducing false positive results and enhancing detection accuracy. Due to a dearth of availability in training data for UAV-based aerial images of obstructions on roadways, synthetic data are generated through data augmentation techniques to bolster model performance. The developed framework achieved a mean average precision (mAP) of 98.5% (mAP 50), which evaluates detection accuracy at an Intersection Over Union (IoU) threshold of 0.5, and 91.2% (mAP 50–95). Results demonstrated improved prediction accuracy with reference images, achieving a 94.67% success rate compared with 48% without them. The methodology enables precise estimation of road usability for various vehicle types, facilitating efficient route planning and debris clearance. This research advances postdisaster roadway assessment by leveraging UAV and photogrammetry techniques, offering a rapid and accurate solution for postdisaster management, and planning for recovery operations.
Chonnapat Opanasopit, Joseph Louis· Journal of computing in civi...· 0 citations
High-resolution spatial data is crucial for riverine modeling and flood mitigation. Traditional data often lacks necessary resolution or flexibility, making Unmanned Aerial Vehicles (UAVs) a transformative solution for generating precise Digital Elevation Models (DEMs). This systematic review analyzes 65 peer-reviewed studies published between 2014 and 2025. Following PRISMA guidelines, studies were selected based on specific inclusion and exclusion criteria focusing on riverine hydraulic applications to evaluate data acquisition methods, spatial accuracy, and operational challenges. The synthesis reveals a standard workflow using multirotor platforms, Structure from Motion (SfM) photogrammetry, and Ground Control Points (GCPs) to feed hydrodynamic models like HEC-RAS. While the literature consistently reports centimeter-level vertical accuracy—ideal for mapping flood inundation—critical challenges persist regarding the optical penetration of dense vegetation and submerged bathymetry. Ultimately, while UAV photogrammetry is a robust spatial analysis tool, advancing high-fidelity riverine analytics requires hybrid approaches integrating technologies like UAV-borne LiDAR and sonar. Creating these seamless topobathymetric models is essential for improving reliable flood risk management and informing effective environmental policy.
Nabil Muhamad, Yassir Arafat, A. Yunar· Journal of Geospatial Scienc...· 1 citation
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.
Abdel-Halem A. Abdel-hamed, Abdallah Samir Abdallah, Ibrahim Elnaml et al.· IOP Conference Series: Earth...· 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
