Evaluating the Accuracy of UAV and TLS for 3D Indoor Modelling in Large-Scale Building Environments

Abstract. In general, poor road conditions, specifically road cracks constitute a public nuisance, causing troublesome to road users, severe damage to vehicles and accidents. Hence, it is essential to detect the road crack earlier as an early-stage preventive for maintenance, but traditional inspection method used in Malaysia to physically collect the road information is extremely time-consuming, hazardous and labour-intensive. Nowadays, new technologies have improved the measuring performance. This leaves a gap in comparing two modern technologies in road cracks mapping. This study aims to explore the quality assessment between the unmanned aerial vehicle (UAV) and terrestrial laser scanning (TLS) methods in road cracks mapping. Two study areas inside the campus of Universiti Teknologi Malaysia (UTM), Johor, were selected. The ground control points (GCPs) and check points (CPs) at the study areas were georeferenced by global navigation satellite system (GNSS) data with MyRTKNet and ISKnet connections. Low altitude aerial imageries were collected using DJI Phantom 4 UAV, while Topcon GLS-2000 was employed to acquire the dense data of the cracked road. GNSS and manual inspection methods were performed to evaluate the results. In terms of mapping and measurement, both TLS and UAV methods produce almost similar results with average RMSE differences ranging from ±0.003 m to ±0.030 m and ±0.042 m to ±0.224 m respectively. This revealed that TLS is more accurate than UAV in mapping and measuring work. Differences in DTM quality across these approaches is below two (2) cm. Based on this study, TLS is more reliable than UAV. However, UAV offers advantages based on several considerations such as cost, time, safety, and accessibility. In summary, findings from this study shed some light to the authorities on the feasibility of UAV and TLS methods in road cracks mapping.

The availability of high-resolution Digital Surface Models of coastal environments is of increasing interest for scientists involved in the study of the coastal system processes. Among the range of terrestrial and aerial methods available to produce such a dataset, this study tests the utility of the Structure from Motion (SfM) approach to low-altitude aerial imageries collected by Unmanned Aerial Vehicle (UAV). The SfM image-based approach was selected whilst searching for a rapid, inexpensive, and highly automated method, able to produce 3D information from unstructured aerial images. In particular, it was used to generate a dense point cloud and successively a high-resolution Digital Surface Models (DSM) of a beach dune system in Marina di Ravenna (Italy). The quality of the elevation dataset produced by the UAV-SfM was initially evaluated by comparison with point cloud generated by a Terrestrial Laser Scanning (TLS) surveys. Such a comparison served to highlight an average difference in the vertical values of 0.05 m (RMS = 0.19 m). However, although the points cloud comparison is the best approach to investigate the absolute or relative correspondence between UAV and TLS methods, the assessment of geomorphic features is usually based on multi-temporal surfaces analysis, where an interpolation process is required. DSMs were therefore generated from UAV and TLS points clouds and vertical absolute accuracies assessed by comparison with a Global Navigation Satellite System (GNSS) survey. The vertical comparison of UAV and TLS DSMs with respect to GNSS measurements pointed out an average distance at cm-level (RMS = 0.011 m). The successive point by point direct comparison between UAV and TLS elevations show a very small average distance, 0.015 m, with RMS = 0.220 m. Larger values are encountered in areas where sudden changes in topography are present. The UAV-based approach was demonstrated to be a straightforward one and accuracy of the vertical dataset was comparable with results obtained by TLS technology.

Purpose. The main objective of this paper is to assess the quality of the 3D model of industrial buildings generated from Unmanned Aerial Vehicle (UAV) imagery datasets, including nadir (N), oblique (O), and Nadir and Oblique (N+O) UAV datasets. Methodology. The quality of a 3D model is defined by the accuracy and density of point clouds created from UAV images. For this purpose, the UAV was deployed to acquire images with both O and N flight modes over an industrial mining area containing a mine shaft tower, factory housing and office buildings. The quality assessment was conducted for the 3D point cloud model of three main objects such as roofs, facades, and ground surfaces using CheckPoints (CPs) and terrestrial laser scanning (TLS) point clouds as the reference datasets. The Root Mean Square Errors (RMSE) were calculated using CP coordinates, and cloud to cloud distances were computed using TLS point clouds, which were used for the accuracy assessment. Findings. The results showed that the point cloud model generated by the N flight mode was the most accurate but least dense, whereas that of the O mode was the least accurate but most detailed level in comparison with the others. Also, the combination of O and N datasets takes advantages of individual mode as the point clouds accuracy is higher than that of case O, and its density is much higher than that of case N. Therefore, it is optimal to build exceptional accurate and dense point clouds of buildings. Originality. The paper provides a comparative analysis in quality of point cloud of roofs and facades generated from UAV photogrammetry for mining industrial buildings. Practical value. Findings of the study can be used as references for both UAV survey practices and applications of UAV point cloud. The paper provides useful information for making UAV flight planning, or which UAV points should be integrated into TLS points to have the best point cloud.

Structural complexity of trees is related to various ecological processes and ecosystem services. To support management for complexity, there is a need to assess the level of structural complexity objectively. The fractal-based box dimension (Db) provides a holistic measure of the structural complexity of individual trees. This study aimed to compare the structural complexity of Scots pine (Pinus sylvestris L.) trees assessed with Db that was generated with point cloud data from terrestrial laser scanning (TLS) and aerial imagery acquired with an unmanned aerial vehicle (UAV). UAV imagery was converted into point clouds with structure from motion (SfM) and dense matching techniques. TLS and UAV measured Db-values were found to differ from each other significantly (TLS: 1.51 ± 0.11, UAV: 1.59 ± 0.15). UAV measured Db-values were 5% higher, and the range was wider (TLS: 0.81–1.81, UAV: 0.23–1.88). The divergence between TLS and UAV measurements was found to be explained by the differences in the number and distribution of the points and the differences in the estimated tree heights and number of boxes in the Db-method. The average point density was 15 times higher with TLS than with UAV (TLS: 494,000, UAV 32,000 points/tree), and TLS received more points below the midpoint of tree heights (65% below, 35% above), while UAV did the opposite (22% below, 78% above). Compared to the field measurements, UAV underestimated tree heights more than TLS (TLS: 34 cm, UAV: 54 cm), resulting in more boxes of Db-method being needed (4–64%, depending on the box size). Forest structure (two thinning intensities, three thinning types, and a control group) significantly affected the variation of both TLS and UAV measured Db-values. Still, the divergence between the two approaches remained in all treatments. However, TLS and UAV measured Db-values were consistent, and the correlation between them was 75%.

Soil roughness represents fine-scale surface geometry which figures in many geophysical models. While static photogrammetric techniques (terrestrial images and laser scanning) have been recently proposed as a new source for deriving roughness heights, there is still need to overcome acquisition scale and viewing geometry issues. By contrast to the static techniques, images taken from unmanned aerial vehicles (UAV) can maintain near-nadir looking geometry over scales of several agricultural fields. This paper presents a pilot study on high-resolution, soil roughness reconstruction and assessment from UAV images over an agricultural plot. As a reference method, terrestrial laser scanning (TLS) was applied on a 10 m x 1.5 m subplot. The UAV images were self-calibrated and oriented within a bundle adjustment, and processed further up to a dense-matched digital surface model (DSM). The analysis of the UAV- and TLS-DSMs were performed in the spatial domain based on the surface autocorrelation function and the correlation length, and in the frequency domain based on the roughness spectrum and the surface fractal dimension (spectral slope). The TLS- and UAV-DSM differences were found to be under ±1 cm, while the UAV DSM showed a systematic pattern below this scale, which was explained by weakly tied sub-blocks of the bundle block. The results also confirmed that the existing TLS methods leads to roughness assessment up to 5 mm resolution. However, for our UAV data, this was not possible to achieve, though it was shown that for spatial scales of 12 cm and larger, both methods appear to be usable. Additionally, this paper suggests a method to propagate measurement errors to the correlation length.

The 3D reconstruction of historical and cultural heritage monuments is a procedure recommended by the UNESCO World Heritage Institution since 1985. It is crucial when conserving monuments and creating digital twins. Current 3D reconstruction techniques using digital images and terrestrial laser scanning (TLS) data are considered as cost-effective and efficient methods for the production of high-quality digital 3D models. In the presented study, laser scanning and close-range photogrammetry techniques and images taken by a low-cost unmanned aerial vehicle (UAV) were applied to quickly and completely acquire the point cloud and texture of a historic church in Poland. The aim of this study was to evaluate two options for integrating TLS and UAV data, using ground control points (GCP) measured by two independent techniques: tachymetry and laser scanning. The study shows that the 3D model created based on ground control points acquired by the laser scanning technique has a mean square error RMSEXYZ = 2.5 cm on the check points. The result obtained is not much larger than the second variant of data integration, for which RMSEXYZ = 1.7 cm. Thus, the TLS method was positively evaluated as a GCP measurement technique for the integration of UAV and TLS data and the creation of cartometric 3D models of religious buildings.

A methodology for optimal volume computation for the environmental management of waste stockpiles was derived by integrating the terrestrial laser scanning (TLS) and unmanned aerial vehicle (UAV) technologies. Among the UAV-based point clouds generated under various flight scenarios, the most accurate point cloud was selected for analysis. The root mean square errors (RMSEs) of the TLS- and UAV-based methods were 0.202 and 0.032 m, respectively, and the volume computation yielded 41,226 and 41,526 m3, respectively. Both techniques showed high accuracy but also exhibited drawbacks in terms of their spatial features and efficiency. The TLS and UAV methods required 800 and 340 min, respectively, demonstrating the high efficiency of the UAV method. The RMSE and volume obtained using the TLS/UAV fusion model were calculated as 0.030 m and 41,232 m3, respectively. The UAV approach generally yielded high point cloud accuracy and volume computation efficiency.

Abstract. Soil roughness represents fine-scale surface geometry which figures in many geophysical models. While static photogrammetric techniques (terrestrial images and laser scanning) have been recently proposed as a new source for deriving roughness heights, there is still need to overcome acquisition scale and viewing geometry issues. By contrast to the static techniques, images taken from unmanned aerial vehicles (UAV) can maintain near-nadir looking geometry over scales of several agricultural fields. This paper presents a pilot study on high-resolution, soil roughness reconstruction and assessment from UAV images over an agricultural plot. As a reference method, terrestrial laser scanning (TLS) was applied on a 10 m x 1.5 m subplot. The UAV images were self-calibrated and oriented within a bundle adjustment, and processed further up to a dense-matched digital surface model (DSM). The analysis of the UAV- and TLS-DSMs were performed in the spatial domain based on the surface autocorrelation function and the correlation length, and in the frequency domain based on the roughness spectrum and the surface fractal dimension (spectral slope). The TLS- and UAV-DSM differences were found to be under ±1 cm, while the UAV DSM showed a systematic pattern below this scale, which was explained by weakly tied sub-blocks of the bundle block. The results also confirmed that the existing TLS methods leads to roughness assessment up to 5 mm resolution. However, for our UAV data, this was not possible to achieve, though it was shown that for spatial scales of 12 cm and larger, both methods appear to be usable. Additionally, this paper suggests a method to propagate measurement errors to the correlation length.

Terrestrial Laser Scanning (TLS) and Unmanned Aerial Vehicle (UAV) Laser Scanning (ULS) are both emerging technologies for rapidly capturing detailed 3D data of structures and environments. This study provides a comparative analysis between these two scanning techniques in terms of the accuracy and differences of the resulting 3D point clouds. A case study was conducted where TLS data was collected from ground-level scan positions while ULS data was captured through automated flights around the facility exterior. The point clouds from each platform were evaluated based on point density, geometric accuracy assessments, and ability to capture fine details. The TLS scans produced a highly accurate and detailed point cloud which was used as a benchmark in this study. The UAV scans exhibited less accuracy when compared to static TLS. However, the UAV was better able to capture hard-to-reach areas and provide a more complete model of the study site exterior. This research provides quantitative and qualitative comparisons between these scanning platforms to help determine the best approach based on requirements. The results will help professionals select the optimal scanning technology for generating the Digital Elevation Models (DEMs) depending on the application and accuracy requirements of the targeted DEMs.

The development of measurement technologies allows for acquiring various data. The Terrestrial Laser Scanning (TLS) technology is frequently combined with classic geodetic measurements – tacheometry or levelling. This article presents a process of the diaphragm wall monitoring during excavation supported with a top-down method. The construction technology applied required proper planning and performance of measurements in difficult construction site conditions in the city centre. TLS allowed for limiting works at daytime and performing monitoring during the night break in works at the construction site as well as limiting the impact of the subsoil process vibrations on the values of displacements and deformations determined. The authors present a comparison of the results of displacement and deformation measurements with a terrestrial laser scanning and tacheometric measurement method. The possibilities of using the data acquired, among others, for the indication of filtration areas, spatial surface deformation analyses and assessment of the wall execution compliance with the design are presented. The analyses carried out show that the TLS may be used in the investment process from the very beginning, being a component of the Building Information Modelling (BIM).

The rapid development of the construction world in Indonesia has led to an increase in supporting technology that is more effective and efficient. The Building Information Model (BIM) technology that begins with the creation of an as-built 3D model, this model describes the existing condition of the building. The Terrestrial Laser Scanner (TLS) method can provide a point cloud with a decent point density, but there are still areas of the building that aren't covered, such as the roof. To be more complete and detailed, additional data is needed using an Unmanned Aerial Vehicle (UAV). The results of the combination of TLS and UAV complement each other so that the results of the point cloud can form more detailed buildings. BIM may be built by combining these two data sets, allowing for the three-dimensional depiction of assets in buildings. The registration results for TLS point cloud data have a fairly good value where the overlap value is 44.9% (minimum 30%), balance is 41.2% (minimum 20%), points < 6mm is 98.9% (minimum 90%). The measurement results using the UAV have an RMSE GCP value of 0.266m and an RMSE ICP of 0.455m. Merging the results of TLS and UAV measurements is done using 3DReshaper software with four align points. The final result of making the BIM model is obtained level of detail (LOD) 3 where room models such as columns, floors, stairs, and walls are well depicted, while asset models such as furniture are also depicted although they are still simple objects.

The complexity that has characterized market relations in recent years, with demands for product and process innovation, has even had repercussions on more traditional activities, such as the civil construction segment. Behind only agriculture, the construction industry represents 13% of the global GDP and its volume is US$ 8 trillion per year. However, construction projects often exceed budget by 80%, and deadlines by 20 months. Between 8% and 10% of productivity gains are related to the insertion of technologies (IPEA, 2022). In the field of civil construction, these digital technologies, such as cloud computing, automation, virtual reality and augmented reality, 3D modeling, communication applications and BIM—Building Information Modeling and even machine learning, have been called Construction 4.0. This article evaluates the feasibility of replacing the use of Terrestrial Laser Scanner (TLS) by the use of Unmanned Aerial Vehicles (UAV). For this, it presents a comparison in the use of equipment for carrying out planimetric surveys in civil construction, using as an example the UAV and the TLS—more modern equipment, in addition to the total station—more conventional equipment—for surveying control points. The results show that in the UAV image processing, the RMSE presented a centimeter accuracy (1.93044 cm) for the model. Even if the accuracy of the models generated by TLS is millimetric, it can be considered that the results obtained here were satisfactory, however it is necessary to apply imaging techniques more efficiently to obtain a more accurate product, in order to arrive at millimeter accuracy. Studies on better positioning of targets and georeferencing of models would also be of great contribution to applications in civil construction.

Both Unmanned Aerial Vehicles (UAVs) and Terrestrial Laser Scanners (TLS) are important techniques for surveying and mapping. UAV equipment is commonly used to collect 2D or 3D data acquisition. Meanwhile, TLS equipment is used for obtaining only 3D data acquisition. However, if both are integrated, they were able to produce more accurate data. Multi-sensor data fusion helps overcome the limitations of a single sensor and enables a complete 3D model for the structure and better object classification. This study focuses on studying the combination of UAV and TLS technologies to collect, process data, and create the complete point cloud between two point clouds of the high building in Ha Long city, Quang Ninh province to establish a 3D model at LoD 3 detail level, with high accuracy. FARO FOCUS3D X130 and DJI Phantom 4 RTK equipments were used to acquire the data in the field. The aerial and ground data were processed using FARO SCENE 2019 and Agisoft PhotoScan software, respectively. The data integration process is done by converting both point clouds into the same coordinate system and then by aligning the same points of both points clouds in Cloud Compare. The result of this study is a 3D model at LoD 3 detail level of the high building based on the point cloud accuracy in centimeter level. The combined use of UAV and TLS technologies has proven to be possible to create a highly accurate 3D model, at the 1:500 scale of urban areas according to current standards.

Deformation detection determines the quantified change of a scene’s geometric state, which is of great importance for the mitigation of hazards and property loss from earth observation. Terrestrial laser scanning (TLS) provides an efficient and flexible solution to rapidly capture high precision three-dimensional (3D) point clouds of hillside areas. Most existing methods apply multi-temporal TLS surveys to detect deformations depending on a variety of ground control points (GCPs). However, on the one hand, the deployment of various GCPs is time-consuming and labor-intensive, particularly for difficult terrain areas. On the other hand, in most cases, TLS stations do not form a closed loop, such that cumulative errors cannot be corrected effectively by the existing methods. To overcome these drawbacks, this paper proposes a deformation detection method with limited GCPs based on a novel registration algorithm that accurately registers TLS stations to the UAV (Unmanned Aerial Vehicle) dense image points. First, the proposed method extracts patch primitives from smoothed hillside points, and adjacent TLS scans are pairwise registered by comparing the geometric and topological information of or between patches. Second, a new multi-station adjustment algorithm is proposed, which makes full use of locally closed loops to reach the global optimal registration. Finally, digital elevation models (DEMs, a DEM is a numerical representation of the terrain surface, formed by height points to represent the topography), slope and aspect maps, and vertical sections are generated from multi-temporal TLS surveys to detect and analyze the deformations. Comprehensive experiments demonstrate that the proposed deformation detection method obtains good performance for the hillside areas with limited (few) GCPs.

With the growing emphasis on sustainability and resource efficiency within the architectural, engineering, and construction (AEC) sectors, Unmanned Aerial Vehicles (UAVs) and Terrestrial Laser Scanner (TLS) have emerged as indispensable tools for the monitoring and inspection of building structures by using 3D modelling. This research is dedicated to assessing the quality and accuracy obtained from 3D modelling for a building and its structural components between UAV photogrammetry and TLS techniques. The investigation involved nadir and oblique flight missions for UAV data acquisition around the target structure, utilising six (6) Ground Control Points (GCPs), while TLS data collection employed direct georeferencing via the traversing method. The results revealed that TLS yielded superior surface reconstruction quality owing to its denser point cloud density, whereas UAV data met the requirements of numerous applications, offering a convenient and economically viable data acquisition solution. Regarding accuracy, a minimal disparity was observed for building objects discernible from both instruments, achieving centimetre-level accuracy. These findings not only highlighted the potential of UAVs and TLS in optimising 3D modelling processes but also offered practical insights for professionals engaged in urban planning, architectural design, and structural analysis endeavours.