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Article

Assessment of UAV Usage for Flexible Pavement Inspection Using GCPs: Case Study on Palestinian Urban Road

by
Ismail S. A. Aburqaq
1,
Sepanta Naimi
1,*,
Sepehr Saedi
1 and
Musab A. A. Shahin
2
1
Department of Civil Engineering, Altinbas University, 34218 Istanbul, Turkey
2
Department of Civil Engineering, Palestine Polytechnic University, Hebron P.O. Box 198, Palestine
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(18), 8129; https://doi.org/10.3390/su17188129
Submission received: 4 June 2025 / Revised: 19 July 2025 / Accepted: 21 July 2025 / Published: 10 September 2025
(This article belongs to the Special Issue Sustainability Assessments and Implementations in Transport Pavement Infrastructure)
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Abstract

Rehabilitation plans are based on pavement condition assessments, which are crucial to modern pavement management systems. However, some of the disadvantages of conventional approaches for road maintenance and repair include the time consumption, high costs, visual errors, seasonal limitations, and low accuracy. Continuous and efficient pavement monitoring is essential, necessitating reliable equipment that can function in a variety of weather and traffic conditions. UAVs offer a practical and eco-friendly alternative for tasks including road inspections, dam monitoring, and the production of 3D ground models and orthophotos. They are more affordable, accessible, and safe than traditional field surveys, and they reduce the environmental effects of pavement management by using less fuel and producing less greenhouse gas emissions. This study uses UAV technology in conjunction with ground control points (GCPs) to assess the kind and amount of damage in flexible pavements. Vertical photogrammetric mapping was utilized to produce 3D road models, which were then processed and analyzed using Agisoft Photoscan (Metashape Professional (64 bit)) software. The sorts of fractures, patch areas, and rut depths on pavement surfaces may be accurately identified and measured thanks to this technique. When compared to field exams, the findings demonstrated an outstanding accuracy with errors of around 3.54 mm in the rut depth, 4.44 cm2 for patch and pothole areas, and a 96% accuracy rate in identifying cracked locations and crack varieties. This study demonstrates how adding GCPs may enhance the UAV image accuracy, particularly in challenging weather and traffic conditions, and promote sustainable pavement management strategies by lowering carbon emissions and resource consumption.

1. Introduction

Road transport remains the primary mode for moving freight and passengers in most countries. Like any infrastructure, roads deteriorate over time due to traffic loads and environmental conditions. As part of asset management programs, road agencies invest considerable effort and resources in assessing pavement surface conditions to ensure serviceability. This data supports informed decisions on maintenance and repair [1,2].
Roadway safety is a major global concern due to the high number of annual fatalities and injuries. Flexible pavements form the backbone of land transportation networks, playing a crucial role in supporting national economies and providing socio-economic benefits [2,3,4].
Traditionally, pavement evaluations depended on visual assessments by specialists and the manual recording of surface conditions for database entry. Recently, automated systems have emerged, using high-resolution vehicle-mounted cameras, digital imaging, and laser sensors to enhance accuracy and reduce subjectivity. However, these ground-based methods remain costly, labor-intensive, time-consuming, and sometimes hazardous for inspectors. They also require specialized personnel and may introduce spatial and assessment errors [5,6].
Pavement maintenance aims to preserve service conditions with minimal disruption to traffic flow [7,8]. In recent years, unmanned aerial vehicles (UAVs) have become valuable tools for 3D modeling, mapping, and pavement inspection [9,10,11]. UAV-captured imagery can be processed to create realistic 3D models, offering detailed insights into pavement conditions. These models help engineers detect issues early and take timely maintenance actions, improving safety and road longevity [12,13,14].
Beyond technical efficiency, UAV-based inspections offer environmental advantages. By reducing the reliance on fuel-powered vehicles and minimizing traffic disruptions, UAVs contribute to lower greenhouse gas emissions and a smaller carbon footprint. This shift aligns with global sustainability goals, particularly in environmentally conscious urban planning. Research indicates that UAV inspections can reduce carbon emissions by up to 90% compared to traditional methods [15,16,17]. Integrating UAVs into pavement management systems supports both technical performance and environmental responsibility [18].
The UAV method not only improves the inspection accuracy and efficiency but also supports sustainable pavement maintenance. By accurately identifying damaged areas, it facilitates targeted rehabilitation, often generating reclaimed asphalt pavement (RAP). These materials can be recycled through plant-mixed cold recycling with emulsified asphalt. The quality of recycled mixes depends on the condition of the original materials, highlighting the importance of a precise pavement evaluation [19,20].

2. Related Works

Tan and Li [9] achieved an accuracy of 1.54 cm in the 3D model created from UAV images, which they used to find road pavement damage with an error rate of 1 cm. Feitosa et al. [10] reviewed the existing research on using UAVs for pavement inspections and noted that recent studies show an accuracy of around 1 cm for 2D measurements and depth measurements in 3D model pavement inspections. Feitosa et al. [10] provided a critical review of the literature on the use of UAVs in pavement inspections and stated that the latest studies indicate an accuracy level of about 1 cm for 2D measurements and depth measurements in the use of the 3D model for pavement inspections. Romero-Chambi et al. [7] compared the use of the 3D model obtained from UAV images taken at different altitudes and demonstrated that 3D characteristics can be measured in the flexible pavements with errors of about 1 cm. Silva et al. [13] concentrated on using drones and deep learning algorithms to tackle the difficulties associated with road maintenance. This approach’s guarantee was proven in a case study employing the PANGEA multi-agent system, which achieved an accuracy of over 95%. This work offers a major step toward more effective and economical road management by highlighting the possibility for more innovation in both the detection models and the drone-based data-gathering procedure. Nappo et al. [21] presented a methodology for detecting asphalt road pavement damage by deriving a hotspot map from 3D point clouds, successfully identifying areas with significant deviations from the reference road surface where anomalies were expected. The proposed approach showed a strong consistency with manually detected damage, validating the effectiveness of the method. The multi-criteria classifier demonstrated the ability to detect longitudinal and transverse cracks wider than 1 cm; though some false positives were observed. These errors were primarily caused by random noise in the point cloud, the presence of concrete structures or ditches at the road edges, and smaller fractures that the procedure could not fully detect. More testing is needed to improve the limits for point clouds with different densities and resolutions, but the results show that the method can successfully find road damage without needing to know what type of damage it is or where it is located.
Recent research has also emphasized the environmental implications of UAV-based pavement inspection. Feitosa et al. [10] pointed out that UAVs enhance data accuracy while simultaneously mitigating environmental effects by obviating the necessity for a fuel-dependent terrestrial survey apparatus. Santos et al. [16] additionally observed that UAVs contribute to the reduction in greenhouse gas emissions by decreasing car traffic and lowering on-site durations. Aurangzeb et al. [17] reported that drone-assisted road inspection initiatives had achieved a decrease of up to 90% in carbon emissions relative to traditional techniques. Wang et al. [18] consider UAVs as a crucial element of sustainable infrastructure monitoring. These contributions illustrate the dual advantage of UAVs in enhancing both technological precision and environmentally sustainable maintenance procedures.
Most previous studies agreed that adding ground control points (GCPs) is expected to increase the accuracy of the generated models, mainly for use on road pavements that include features or reference points that are accessible before taking images [7,9,10,22,23].
In the field of mapping, unmanned aerial vehicle (UAV) technology has become one of the most quickly emerging technologies and one of the most widely employed technologies. UAVs are widely used in civilian applications as well as applications for the Department of Survey and Mapping [24,25]. Therefore, to guarantee accuracy while using a UAV to inspect flexible pavements, ground control points (GCPs) are used [7,9,10,26]. This approach will increase the accuracy of the images that are used to identify pavement issues, such as cracks, potholes, and pavement surface erosion [26]. Using photogrammetry software can help create detailed maps, height models, or 3D models of the project area by processing the necessary data. The maps can be employed for data extraction purposes, such as obtaining volumetric measurements or extremely accurate distances. Drones are able to fly at far lower altitudes than manned flights or satellite imagery, which allows for the collection of high-resolution, high-accuracy data that is significantly quicker, less expensive, and independent of climatic factors such as cloud cover [27,28].
The pavement may deteriorate and require expensive repairs and resurfacing if these distresses are not addressed and remedied. Because these losses are cumulative, it is essential to perform maintenance at the appropriate time to prevent further deterioration [29]. As shown in Figure 1, there are several cases of flexible pavement distress, such as rutting [24], cracks [22], and patches and potholes [30], along with the various symptoms of this condition. The identification and repair of these distresses is essential for the preservation of the integrity of the pavement, the safety of the pavement, and the low costs of the maintenance [12,29].

3. Materials and Methods

The method employed in this study involves a structured process to evaluate flexible pavement distress using UAV-based imaging and ground control points (GCPs). It integrates both fieldwork and data processing to ensure accurate results. The process begins with site reconnaissance and planning, followed by data collection and image capture using UAVs. Data is then processed using Agisoft Photoscan Agisoft (Metashape Professional (64 bit)) software to generate a 3D model, which is analyzed to identify and quantify pavement distress. The following steps outline the detailed procedure used in this method [31].
The first step is related to site reconnaissance and planning. It consists of some actions, such as selecting the area of study, choosing the location of ground control points (GCPs), marking GCPs, and monitoring them using a Spectra Precision SP60 GNSS receiver (Spectra Geospatial, Westminster, CO, USA). The second step is data collection, involving a manual measurement method using manual measuring tools, such as a meter, scale, ruler, and string, and preparing for the process of photo-graphing the study area using UAVs, which includes selecting a suitable drone and a suitable time for photographing. The third step is capturing the images from UAVs and processing the data by using the Agisoft Photoscan program. The last step involves the analysis and comparison of data to obtain the results.
This methodology not only ensures high technical accuracy but is also deliberately designed to reinforce environmental sustainability. By utilizing UAVs instead of conventional ground-based inspection methods, the workflow minimizes fuel consumption, reduces vehicle emissions, and avoids unnecessary disruption to local traffic and the surrounding environment. As such, the methodology itself reflects a low-carbon approach to pavement assessment—one that aligns with sustainable infrastructure practices and current global environmental priorities [15,16,17,18].

3.1. Study Area

The selected area of the case study is a part of the road located at the part of Al-Dahrieh International Stadium Street/Hebron, Palestine. The geographical location of the beginning of the study area lies at 31°25′22.8″ N and 34°57′06″ E, and the geographical location of the end of the study area lies at 31°25′12.8″ N and 34°56’24.6″ E. This street was chosen due to the lack of vehicles passing on it compared to the main roads, and it is easy to perform the manual measuring for pavement detection on it (Figure 2).
Paved roads serve a wide range of transportation needs across different environmental and operational conditions. Roads that have been recently built or that have been well maintained should have great surface conditions and allow for traffic that is both smooth and safe. Nevertheless, traffic, weather, and poor driving habits destroyed the paved road surface. If there is significant traffic and frequent use, roads will sustain damage more quickly and frequently. Water trapping on the road can cause ruts and potholes. The paved road had surface distress, which made it impossible for the local community to interact with one another in their day-to-day activities. The study locations included a few examples of potholes. The paved road had holes in its surface where the potholes were located. Some examples of potholes that were found in this research are depicted in Figure 3.
Pavement inspections were traditionally carried out using manual methods for a considerable amount of time using the traditional manner. The manual approach involves proposing to section the road into several inspection units along its length and width to accomplish a comprehensive assessment. The necessary tools required for conventional (manual methods) measurement include a ruler, a straight edge, a meter tape to measure the length of the ruts, and a thread to measure the area of the pothole [24]. Additionally, the process of manual measurement can be highly laborious and time-consuming. Figure 4 provides an example of the conventional method for measuring rutting depth.

3.2. Reconnaissance and Planning

The topography of the area and the pavement condition were studied [21]. To guarantee that the ground control points were located in a manner that spanned all elevations and were scattered in a manner that assured they covered all sides of the study area, the appropriate position for the ground control points was chosen. It can be seen in Figure 5 that the control points have been specified. We utilized elements that are easily discernible on the pavement surfaces, such as crack crossings, sharp edges, and stains, among other things. Therefore, to achieve GCPs that were of a high level of precision, the GCPs were monitored with the use of a Spectra Precision SP60 GNSS device [5]. Table 1 displays the coordinates of several GCPs.

3.3. Data Collection

A UAV (DJI Mavic 3, L2D-20c camera, Hasselblad, Gothenburg, Sweden) was used to capture 263 aerial photographs during multiple flight missions over the designated test area, maintaining a consistent flight altitude of approximately 59 m. Data acquisition was conducted under optimal environmental conditions, including clear skies, low wind speeds, and favorable natural lighting during early morning hours. These conditions were deliberately selected to minimize image distortion, shadows, and glare, thereby enhancing the visual clarity and reliability of the captured data. To ensure high spatial accuracy, six ground control points (GCPs) were strategically distributed across the site and measured using a real-time kinematic (RTK) positioning system, providing precise geographic coordinates. The integration of GCPs facilitated accurate georeferencing, scale, and orientation alignment within the target coordinate system, which was critical for generating high-quality orthomosaics and 3D models for pavement condition assessment. Figure 6 provides an example of the UAV imagery used in this study.

3.4. Data Processing

Agisoft Photoscan was used to process the images, which were obtained from UAVs in two phases. The first process was to obtain a 3D model without using GCPs. In the second phase, the 3D model and the orthophoto were reconstructed using GCPs with a ground resolution of 1.47 cm/pix (Figure 7). Many features of Agisoft Photoscan are used to extract digital elevation models (DEMs) and dense point cloud data (coordinate-based data collection). The exterior surface of an item is commonly represented by these points in a 3D coordinate system using X, Y, and Z coordinates. A 3D point cloud was utilized to represent volumetric data and generate contour lines. Dense point cloud data generates high-accuracy results [24]. Table 2 shows the RMSE of the coordinates of the control points in X (easting), Y (northing), and Z (altitude).

4. Results

This study evaluates the accuracy of a generated three-dimensional (3D) model in detecting flexible pavement distress using a limited sample size. This study compares the traditional measurement method with the 3D model-based approach. The 3D model and orthophoto were employed to assess pothole and patch areas by digitizing polygons, measuring rutting by placing points at regular intervals, and identifying crack regions and types through Agisoft Photoscan software tools.

4.1. Rutting Measurement

Table 3 shows the length of the rutting measurement. There were ten samples, named S1, S2, S3, S4, … S10. They were measured using two methods: the conventional (field) measurement (Figure 8) and the modal measurement (Figure 9). The differences between them were calculated considering the field measurements as the correct standard measurements, and it was found that the maximum error value is 5 mm and the minimum error value is 1 mm in elevations. It was also found that the RMSE is 0.00354 m, which is 96.46%. Among the ten samples analyzed, four showed errors above the RMSE threshold, while six showed errors below it.
In Figure 10, the differences between the standard field measurement and the proposed UAV-based model measurement are small. Both methods yield accurate results, suggesting they are comparable. However, the proposed technique yielded advantages in the rut depth detection and measurement. Comparing the error margin of the old strategy to the error rate of the recommended method shows that the latter stays low and falls well inside an acceptable range. Traditional approaches, which rely on manual procedures and human judgment, are more prone to cause inconsistencies and mistakes.
These errors might be caused by visual perception restrictions, measurement technique inconsistencies, and human data-gathering inaccuracy. Each of these factors can affect the findings’ accuracy. Human errors are unavoidable and can greatly reduce the dependability of field-based evaluations [32,33].

4.2. Patch Measurements

Table 4 presents the measurements of ten patch samples (Pa1 to Pa10). They were measured using two methods: the conventional (field) measurement (Figure 11) and modal measurement (Figure 12). The results reveal that the relative standard error (RMSE) is 0.0444 m2, which is equivalent to an accuracy rate of 99.6%. The fact that this is the case indicates that the data is typically dependable and indicates a high level of accuracy. However, three samples had error values that were more than the RMSE, which indicates that there may have been irregularities in the conditions under which the samples were measured or sampled.
The error values for the remaining seven values were less than the RMSE, meaning they were closer to the expected values. These differences imply that although the dataset as a whole is consistent, there are some minor inconsistencies that may be addressed by improving the calibration or by increasing the number of controlled conditions to increase the accuracy of measurements in future studies.
Figure 13 shows that the given approach is accurate enough to reflect rut and pothole dimensions and areas. Its precision is comparable to established techniques for measuring these regions using surveying instruments or measurement tools, proving its trustworthiness. Total stations, laser scanners, and manual measuring devices are accurate surveying instruments, but they are also time-consuming, laborious, and prone to human mistakes. In contrast, Figure 13 indicates that current technology or automated approaches may equal the precision of this conventional equipment and simplify the process, making it more efficient. Both the conventional and new methods accurately describe the size and area of ruts and potholes, but the latter’s reduced human error and faster data processing make it an attractive choice.
The novel technology may improve the rut and pothole measuring precision and efficiency, providing a trustworthy solution for applications that demand constant and precise road condition monitoring. Thus, both approaches can produce precise measurements, However, the proposed method offers greater practicality in real-world applications.

4.3. Crack Measurement and Classification

Figure 14 shows that the method can accurately identify cracked and non-cracked areas on surfaces with an impressive accuracy rate of up to 96%, allowing for a detailed and reliable assessment of pavement conditions, making it highly effective for road maintenance and management by ensuring that problem areas are correctly identified and addressed in a timely manner. The method can find cracks and sort them into types like crocodile cracks, longitudinal cracks, transverse cracks, and diagonal cracks. This is important for understanding how much damage there is and what kind of stress the surface has faced over time, giving a clearer view of the pavement’s condition. It can also measure the width of the cracks accurately, with a precision of about 1 cm, which is more than enough for a detailed analysis of the cracks’ state and for figuring out how these issues might get worse if not fixed quickly. By providing detailed measurements of both the crack types and their widths, the method offers valuable insights into the severity of the damage, enabling more informed decisions regarding the necessity for repairs and helping prioritize interventions based on the urgency and impact; it further supports the decision-making process on the most appropriate repair methods, whether surface treatments, patching, or a more extensive reconstruction, ensuring that maintenance efforts are both cost-effective and appropriately targeted to maximize the lifespan and performance of the infrastructure; overall, the ability to identify and measure cracks with such accuracy offers significant advantages in road management, enabling timely interventions that help prevent further deterioration and extend the lifespan of road infrastructure while optimizing the use of maintenance resources. Figure 15 shows fractured and uncracked surfaces before identification.
In this study, a site-specific quantitative assessment was conducted to evaluate the environmental benefits of the UAV-based pavement inspection method compared to traditional ground vehicle inspections. The conventional inspection process typically involves a team using three vehicles to cover an approximate route length of 50 km. Each vehicle consumes around 10 L of diesel per 100 km, resulting in a total fuel consumption of approximately 15 L per inspection cycle. Considering that diesel combustion emits roughly 2.68 kg of CO2 per liter, the total CO2 emissions for one inspection cycle are estimated at about 40.2 kg. In contrast, a single UAV flight consumes approximately 0.5 kWh of electrical energy. Assuming an emission factor of 0.5 kg CO2 per kWh based on the local electricity grid, the CO2 emissions per UAV flight are approximately 0.25 kg. This represents a reduction in carbon emissions exceeding 99% compared to the traditional method, demonstrating the substantial environmental advantages of employing UAV technology for pavement inspections.

5. Discussion

The results of this study indicate that the proposed method—utilizing unmanned aerial vehicles (UAVs) combined with ground control points (GCPs)—provides a significant improvement over traditional pavement inspection techniques, particularly in terms of accuracy, operational efficiency, and safety. This enhancement is especially important in urban environments, where rapid assessments with minimal traffic disruption are essential. Unlike the manual method, which typically requires a full team (two individuals for measurement and two for traffic control), the UAV-based approach can be executed by a single trained operator. This significantly reduces labor requirements, minimizes safety risks for personnel, and accelerates the data collection process. The reduction in field personnel not only decreases operational costs but also lowers the exposure to roadside hazards, enhancing worker safety. UAVs can capture high-resolution images in a fraction of the time required by conventional methods, enabling more frequent and widespread monitoring of road infrastructure.
In the conventional method, a full team of three to four people is required—two individuals to take measurements and two others to manage and control the traffic around the inspection area to ensure safety during the measurement phase. Such coordination demands careful planning, traffic signage, and often requires temporary traffic control measures, which can inconvenience road users and extend project timelines. This method is not only time-consuming but also resource-intensive, requiring coordination among several people and the use of traffic management measures. It also limits the number of road segments that can be inspected within a given timeframe. In contrast, the proposed UAV-based method requires only one experienced individual to operate the UAV and collect the necessary data, making the process far more streamlined and flexible. Furthermore, UAV operations can be scheduled during off-peak hours or in difficult-to-access areas without needing extensive ground logistics. This results in a faster data collection phase, as UAVs can capture high-resolution images and measurements of the pavement surface in a fraction of the time that traditional methods take, thereby supporting timely maintenance planning and decision-making.
Furthermore, the UAV-based method enhances safety. Since data is collected from above, inspectors are not exposed to on-road hazards, which is especially important on high-traffic roads or in dangerous conditions. UAVs also allow for continuous monitoring without interrupting traffic, unlike conventional methods that require full or partial road closures. In terms of measurement quality, the UAV method achieves a high precision. For example, rut depth measurements in this study had an error margin of 3.54 mm, which is consistent with Tan and Li [9], who reported an accuracy of 1 cm.
These findings align with previous studies by Feitosa et al. [10] and Romero-Chambi et al. [7], which confirmed the reliability of UAVs in detecting various pavement distresses such as cracks, potholes, and rutting. These studies also emphasized the time and cost efficiency of UAV-based approaches. Our results support these conclusions, as different types of pavement damage were successfully identified and measured with a high accuracy.
The findings of this study align with previous research, including the work of Romero-Chambi et al. [7], highlighting the reliability of this method in accurately identifying and quantifying various surface distresses, such as cracks and depressions. The high level of measurement accuracy enables the development of advanced maintenance management systems based on precise data, which can lead to optimized resource allocation and improved strategic decision-making in pavement maintenance. To ensure the proper implementation of this method, it is crucial to provide adequate training for operators and to establish necessary coordination with relevant authorities. Ultimately, through a thorough analysis of imaging results, an accurate identification of distresses, and a clear understanding of the primary causes of pavement deterioration by experts, the findings of this research can serve as a foundation for expanding the application of drone technology in the intelligent and sustainable management of transportation infrastructure.
The detection and classification of pavement cracks, which was conducted with an accuracy of 1 cm in this study, further corroborates the work of Nappo et al. [21], who also demonstrated that UAVs could effectively identify and categorize various types of pavement cracks, such as longitudinal, transverse, and crocodile cracks. The ability to detect these cracks with a high accuracy is crucial for proactive pavement management, as it allows for the early identification of areas that may require maintenance or repairs.
The results of this study show that the UAV-based method is highly effective in classifying cracks and can distinguish between different types, providing valuable information for road maintenance teams. The RMSE values for rut depth measurements (3.54 mm) and patch area measurements (4.44 cm2) in this study were found to be comparable to those reported in similar studies, which further supports the accuracy of the UAV-based method for pavement distress detection.
However, some slight discrepancies were observed in patch area measurements compared to the findings of Silva et al. [13], where the RMSE for some patches in this study was slightly higher. These discrepancies may be attributed to differences in the UAV models used, the imaging resolution, or other factors such as environmental conditions during data collection. Nevertheless, the overall accuracy of the proposed method is still considered to be within an acceptable range and is sufficient for practical pavement inspection purposes.
Besides its technological and operational advantages, the UAV-based approach illustrated in this paper provides concrete environmental benefits. The inspection technique markedly decreases the fuel consumption, CO2 emissions, and noise pollution by obviating the necessity for vehicle convoys, heavy machinery, and on-site personnel. These savings are particularly significant when inspections are conducted routinely or over extensive metropolitan networks. As evidenced in analogous studies by Feitosa et al. [10] and Santos et al. [16], the utilization of UAVs facilitates the advancement of low-carbon infrastructure monitoring systems. Aurangzeb et al. [17] observed that drone-assisted inspection initiatives had realized emission reductions of up to 90% relative to conventional methods. This affirms the method’s function as both a technological breakthrough and a sustainable practice that corresponds with worldwide environmental objectives. Moreover, the UAV’s ability for non-intrusive surveillance guarantees little disturbance to ecosystems and urban traffic dynamics, enhancing its use as an instrument for sustainable pavement management [18].
Beyond the immediate benefits in accuracy and operational efficiency, integrating UAV-based inspections into routine municipal asset management can lead to predictive maintenance planning. By continuously collecting standardized image data, cities can develop deterioration models that inform when and where maintenance should occur. This shift from reactive to proactive strategies may reduce lifecycle costs and improve road safety.
The results of this study clearly demonstrate that the integration of unmanned aerial vehicles (UAVs) with ground control points (GCPs) enables highly accurate assessment of pavement conditions. This combined approach not only enhances the measurement precision but also paves the way for substantial future improvements through the incorporation of advanced artificial intelligence (AI) and deep learning algorithms. By leveraging AI techniques, the system’s capability to automatically detect, classify, and diagnose various pavement distresses—such as cracks, potholes, and rutting—can be significantly enhanced. This advancement would facilitate more efficient, large-scale road monitoring and maintenance operations, reducing the reliance on manual inspections and enabling proactive infrastructure management. Consequently, the methodology developed in this study establishes a robust foundation for ongoing innovation and technological progress in the field of pavement inspection and maintenance.
Although this research was conducted on a relatively low-traffic urban road segment, the UAV-GCP methodology shows strong potential for applications in more complex environments characterized by higher traffic volumes and a denser urban infrastructure. An effective deployment in such settings would require careful coordination with municipal authorities and traffic management agencies to schedule data acquisition during periods of minimal traffic disruption and in compliance with local regulations. This institutional collaboration is essential to ensure both safety and operational efficiency. With proper planning and stakeholder engagement, the UAV-based inspection system can be successfully scaled and adapted to address the challenges posed by diverse urban contexts, including areas with significant obstructions, dynamic traffic patterns, and regulatory constraints.
A critical factor in achieving the high accuracy and reliability of UAV-based pavement inspections lies in the strategic selection and deployment of ground control points (GCPs), alongside the careful timing of image acquisition flights. The optimal placement of GCPs must account for site-specific environmental factors to maximize their visibility and minimize issues such as occlusions caused by shadows, glare, or surface reflectance, which can adversely affect the image quality and model accuracy. Additionally, conducting UAV flights under favorable lighting and weather conditions—such as clear skies, low wind speeds, and diffuse sunlight—is essential to reduce image distortion and improve data fidelity. Such meticulous operational planning and environmental consideration are indispensable for mitigating measurement biases, enhancing the robustness of the data collected, and ensuring high-fidelity outcomes that are suitable for practical pavement condition assessments and maintenance decision-making.

6. Conclusions

This study confirms the effectiveness of using unmanned aerial vehicles (UAVs), enhanced by ground control points (GCPs), for the accurate detection and measurement of flexible pavement surface distresses. The method demonstrated a high precision in capturing rut depths and patch areas, presenting a reliable, cost-effective, and safer alternative to traditional inspection techniques, particularly in challenging weather or high-traffic environments. In addition to its technical and operational advantages, the UAV-based approach contributes significantly to sustainable infrastructure management by minimizing the need for fuel-dependent equipment, reducing greenhouse gas emissions, and limiting the disruption to traffic and surrounding environments. These environmental benefits align with global goals for low-carbon pavement infrastructure and sustainable development. While some challenges remain, such as regulatory constraints and the potential variability in the data quality due to environmental factors, this research lays a strong foundation for the broader implementation of UAV technology in pavement maintenance programs. Future research should further explore the optimization of UAV operations under varying field conditions and the impact of the flight height and image processing algorithms on 3D model accuracy. The findings support UAVs not only as an efficient inspection tool but also as a step toward more environmentally responsible and sustainable road management practices.

Author Contributions

Conceptualization, I.S.A.A., S.N., S.S. and M.A.A.S.; Methodology, I.S.A.A., S.N., S.S. and M.A.A.S.; Software, I.S.A.A., S.N., S.S. and M.A.A.S.; Validation, I.S.A.A., S.N., S.S. and M.A.A.S.; Formal analysis, I.S.A.A., S.N., S.S. and M.A.A.S.; Investigation, I.S.A.A., S.N., S.S. and M.A.A.S.; Resources, I.S.A.A., S.N., S.S. and M.A.A.S.; Data curation, I.S.A.A., S.N., S.S. and M.A.A.S.; Writing—original draft, I.S.A.A., S.N., S.S. and M.A.A.S.; Writing—review & editing, I.S.A.A., S.N., S.S. and M.A.A.S.; Visualization, I.S.A.A., S.N., S.S. and M.A.A.S.; Supervision, I.S.A.A., S.N., S.S. and M.A.A.S.; Project administration, I.S.A.A., S.N., S.S. and M.A.A.S.; Funding acquisition, I.S.A.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Acknowledgments

We appreciate the support in obtaining data from the Al-Dhahiriya Municipality, which oversees the study area. Special thanks to the Graduate Studies Department and the Civil Engineering Department at Altinbas University, Turkey.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

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Figure 1. Samples of flexible pavement distress.
Figure 1. Samples of flexible pavement distress.
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Figure 2. Study area, source (Al-Dahrieh Municipality). Note: The study area is marked with a red line.
Figure 2. Study area, source (Al-Dahrieh Municipality). Note: The study area is marked with a red line.
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Figure 3. Sample of ruts, patches, and cracks (captured by camera).
Figure 3. Sample of ruts, patches, and cracks (captured by camera).
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Figure 4. Example of measuring rut length with manual method using meter and ruler (captured by camera).
Figure 4. Example of measuring rut length with manual method using meter and ruler (captured by camera).
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Figure 5. GCP locations and error estimates.
Figure 5. GCP locations and error estimates.
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Figure 6. Sample of UAV images.
Figure 6. Sample of UAV images.
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Figure 7. An orthophoto with a coordinate system.
Figure 7. An orthophoto with a coordinate system.
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Figure 8. The measurement for the depth of the rut using conventional methods.
Figure 8. The measurement for the depth of the rut using conventional methods.
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Figure 9. The measurement for the depth of the patch using modal methods. Note: The green-bordered point represents the normal road surface elevation, while the red-bordered point indicates the depressed surface elevation due to rutting, both generated using Agisoft Metashape tools.
Figure 9. The measurement for the depth of the patch using modal methods. Note: The green-bordered point represents the normal road surface elevation, while the red-bordered point indicates the depressed surface elevation due to rutting, both generated using Agisoft Metashape tools.
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Figure 10. Difference in values of ruts.
Figure 10. Difference in values of ruts.
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Figure 11. The measurement of the area of patches using the conventional method.
Figure 11. The measurement of the area of patches using the conventional method.
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Figure 12. The measurement of the area of patches using the modal method. Note: The number inside the red frame represents the calculated area of the patches.
Figure 12. The measurement of the area of patches using the modal method. Note: The number inside the red frame represents the calculated area of the patches.
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Figure 13. The graph of the difference in area errors of patches.
Figure 13. The graph of the difference in area errors of patches.
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Figure 14. A sample of the crack regions. Note: The numbers and black lines in the figure indicate the locations of cracks identified in this section of the pavement.
Figure 14. A sample of the crack regions. Note: The numbers and black lines in the figure indicate the locations of cracks identified in this section of the pavement.
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Figure 15. The cracked and non-cracked areas before the crack mapping process.
Figure 15. The cracked and non-cracked areas before the crack mapping process.
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Table 1. GCPs coordinates (with Palestine 1923/Palestine Grid (EPSG:28191) coordinate system).
Table 1. GCPs coordinates (with Palestine 1923/Palestine Grid (EPSG:28191) coordinate system).
PointsEastingNorthingHeight/Elevation
C1145,374.61592,370.085634.651
C2145,231.01192,321.820629.327
C3145,081.41292,305.848628.150
C4144,927.75492,265.035626.278
C5144,795.49992,223.948623.577
C6144,600.31192,157.798624.056
Table 2. Control points errors and RMSE.
Table 2. Control points errors and RMSE.
LabelEasting Error (mm)Northing Error (mm)Height/Elevation Error (mm)Total (mm)Image (pix)
C11.722−2.630−1.4493.4620.849 (4)
C2−3.4275.6470.4576.6220.543 (4)
C31.913−6.3640.3116.6530.438 (4)
C4−0.6324.410−4.7446.5081.061 (3)
C5−0.5600.3383.7453.8020.805 (4)
C60.975−1.457−4.1274.4851.761 (3)
Total1.8274.1033.0545.4320.956
Table 3. Measurements for the depth of the rut samples.
Table 3. Measurements for the depth of the rut samples.
Sample No.Values from Conventional Measurement (mm)Values from the Modal
Measurement (mm)		Error (mm)
S176771
S261654
S362602
S441454
S555583
S640433
S736415
S842475
S975772
S1041454
RMSE = 3.54 mm = 0.00354 m
Table 4. Measurements for the area of patch samples.
Table 4. Measurements for the area of patch samples.
Sample No.Values from Field Measurement (m2)Values from Modal
Measurement (m2)		Error
(m2)
Pa111.53811.4810.057
Pa21.9962.0150.019
Pa358.24558.1360.109
Pa44.1464.1260.020
Pa53.2433.1920.051
Pa62.5162.5420.026
Pa72.7922.7820.010
Pa822.05322.0660.013
Pa94.7684.7710.003
Pa102.3712.3560.015
RMSE = 4.44 cm2 = 0.0444 m2
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Aburqaq, I.S.A.; Naimi, S.; Saedi, S.; Shahin, M.A.A. Assessment of UAV Usage for Flexible Pavement Inspection Using GCPs: Case Study on Palestinian Urban Road. Sustainability 2025, 17, 8129. https://doi.org/10.3390/su17188129

AMA Style

Aburqaq ISA, Naimi S, Saedi S, Shahin MAA. Assessment of UAV Usage for Flexible Pavement Inspection Using GCPs: Case Study on Palestinian Urban Road. Sustainability. 2025; 17(18):8129. https://doi.org/10.3390/su17188129

Chicago/Turabian Style

Aburqaq, Ismail S. A., Sepanta Naimi, Sepehr Saedi, and Musab A. A. Shahin. 2025. "Assessment of UAV Usage for Flexible Pavement Inspection Using GCPs: Case Study on Palestinian Urban Road" Sustainability 17, no. 18: 8129. https://doi.org/10.3390/su17188129

APA Style

Aburqaq, I. S. A., Naimi, S., Saedi, S., & Shahin, M. A. A. (2025). Assessment of UAV Usage for Flexible Pavement Inspection Using GCPs: Case Study on Palestinian Urban Road. Sustainability, 17(18), 8129. https://doi.org/10.3390/su17188129

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