This paper presents a deterministic system that combines textual semantic data from panoramic images with LiDAR point clouds in a mobile mapping setup. Urban scenes often include textual elements, such as signs and business names, that provide key details typically missing from LiDAR-based urban digital twins. The presented method uses deep learning-based OCR to extract text from street panoramas and then categorizes it into urban types using a rule-based classifier. Text regions are geometrically projected into the LiDAR environment by converting image coordinates into viewing rays that intersect LiDAR surfaces, such as facades. Data from multiple panoramas are merged with confidence-weighted spatial clustering to produce consistent semantic markers for urban features. Extracted business names enable text-based searches of the LiDAR point cloud, allowing facility location by category, keyword, or brand. Tests on datasets from European and U.S. cities support plausible facade-level localization and demonstrate the framework’s ability to enhance LiDAR point clouds with searchable semantic information. The main contribution is not a new standalone OCR or LiDAR-processing algorithm, but a deterministic multimodal integration framework that combines deep-learning OCR, geometric back-projection, and cross-view spatial fusion to convert street-level textual cues into reliable, queryable 3D semantic markers within mobile-mapping LiDAR data.

The configuration of a multi-LiDAR system impacts coverage, redundancy, and observability in mobile mapping. In this study, a multi-LiDAR configuration is modeled as a constrained optimization problem that considers symmetry and clearance constraints. A physics-based simulation is applied to evaluate coverage, overlap, and angular diversity for spinning LiDARs such as the Ouster OS1-64 and the Velodyne VLP-16. Three methods of Bayesian Optimization (BO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) are used. In an indoor space, all methods find symmetric multi-sensor configurations that maximize coverage and redundancy. GA and PSO methods required thousands of evaluations, whereas BO demonstrated excellent efficiency by converging in fewer iterations. Validation using simulated, realistic trajectories and ground-truth environments shows that symmetric multi-LiDAR configuration increases surface completeness by 10-11% over single-sensor setups (up to 27% for OS1-64 and 42% for VLP-16). The results further show that bilateral symmetry is a practical mounting constraint and also a robust design principle that improves mapping completeness.

Video-based photogrammetric reconstruction is increasingly used when high-resolution still images are unavailable. However, limited spatial resolution, compression artifacts, and motion blur often reduce geometric accuracy. Recent advances in learning-based image super-resolution (SR) offer a promising preprocessing method, but their geometric reliability within photogrammetric workflows remains not well understood. This study provides a controlled quantitative evaluation of learning-based super-resolution for video-based 3D reconstruction. Low-resolution video frames are enhanced using two representative methods: an open-source real-world SR model (Real-ESRGAN & times;4) and a commercial solution (Topaz Video AI & times;4). All datasets are processed with the same Structure-from-Motion and Multi-View Stereo pipelines and tested against terrestrial laser scanning (TLS) reference data. Results show that super-resolution significantly increases reconstruction density and improves the recovery of fine-scale surface details, while also leading to greater local surface variability compared with reconstructions from the original video; photogrammetric stability remains consistent despite these changes. The findings highlight a fundamental trade-off between reconstruction completeness and local geometric accuracy and clarify when enhanced video imagery via super-resolution can be a reliable source for 3D reconstruction. These results are especially important for spatial data science workflows and AI-powered 3D modeling and digital twin applications.

Ground Control Points (GCPs) are essential for accurate georeferencing of optical imagery; however, their selection is often heuristic and affected by temporal changes in image geometry. This challenge is particularly acute for Google Earth imagery, where acquisition conditions and mosaicking processes vary over time. This paper presents a multi-epoch robust framework for the automatic design of GCP networks to precisely georeference multi-temporal Google Earth images. GCP selection is formulated within an affine optimal experimental design setting, in which candidate configurations are evaluated against the most challenging acquisition epoch to promote consistency over time. A hybrid DI-optimality criterion balances transformation stability and interior prediction accuracy without requiring interior control points. The framework also includes an automated method for determining the optimal number of GCPs using marginal-gain stopping and cost-regularized μ-sweep analysis. Experiments on two urban case studies show that compact, well-conditioned GCP networks can match the accuracy of larger heuristic networks and achieve top 10% root-mean-square error (RMSE) performance on a random feasible subset benchmark. Results demonstrate that a carefully designed GCP network can greatly reduce the number of control points needed while maintaining stable geometric performance across acquisition sessions.

This paper introduces a constraint-aware optimization framework for designing spherical multi-camera rigs that achieve complete panorama coverage while adhering to physical and field-of-view limitations. The approach assesses coverage using solid-angle geometry and calculates the sampling density in pixels per steradian, providing a measurable, traceable basis for panoramic optical measurement. By viewing panoramic imaging as a directional measurement challenge, the framework aligns with principles of optical metrology and guarantees uniform, non-contact optical sensing around the sphere. The optimization process includes capsule-based collision constraints, soft coverage losses, and field-of-view intersection modeling to produce physically feasible rig configurations. Experiments show that the optimized rigs provide improved coverage uniformity and less redundancy, with validation through Blender-generated synthetic panoramas confirming the practical performance of the designed optical systems. The proposed approach allows for systematic, measurement-driven design of spherical camera rigs for use in immersive imaging, robotic perception, and structural inspection.

Accurate 3D reconstructions of AEC structures using UAV photogrammetry are often hindered by occlusions, excessive image overlaps, or insufficient coverage, leading to inefficient flight paths and extended mission durations. This work presents a BIM-aware, autonomous UAS (Unmanned Aerial System) trajectory generation framework wherein a compact, geometrically valid viewpoint network is first derived as a foundation for path planning. The network is optimized via Integer Linear Pro-gramming (ILP) to ensure coverage of IFC-modeled components while penalizing poor stereo geometry, GSD, and triangulation uncertainty. The resulting minimal network is then sequenced into a global path using a Traveling Salesman Problem (TSP) solver and partitioned into battery-feasible epochs for operation on active construction sites. Evaluated on two synthetic and one real-world case study, the method produces au-tonomous UAV trajectories that are 31–63% more compact in camera usage, 17–35% shorter in path length, and 28–50% faster in execution time, without compromising coverage or reconstruction quality. The proposed integration of BIM modeling, ILP op-timization, TSP sequencing, and endurance-aware partitioning enables the framework for digital-twin updates and QA/QC monitoring. Accordingly, offering a unified, ge-ometry-adaptive solution for autonomous UAV inspection and remote sensing.

In construction automation applications, coarse registration between 3D Building Information Modelling (BIM) and the as-built point cloud is vital for the monitoring of construction progress. This can be achieved by extracting highly distinct geometric features in both datasets to speed up the correspondence search. However, the existing geometric feature-based coarse registration methods have limitations in the Architecture, Engineering, Construction & Facility Management (AEC/FM) context because building designs often contain a considerable self-similarity, symmetry, and lack of texture. In this work, we propose an automatic coarse registration method that is motivated by the Random Sample Consensus (RANSAC) algorithm to estimate the transformation parameters that best align the as-built point cloud in the coordinate frame of the BIM model by matching the corresponding columns. The method is based on the extraction of columns from the as-built point cloud and the as-planned BIM model. For the point cloud data, fully automated column extraction techniques are used by applying deep learning, whereas the BIM model columns are extracted from the available semantic information. Experiments are carried out on real-life datasets from the building construction site to validate the proposed method. The results show that our proposed column-based registration method achieved an RMSE of 2 centimeters, and the cloud-to-cloud mean distance of 1.6cm +/- 1.8cm after fine registration. The accuracy of the co-registration result shows that our proposed approach contributes to automating the registration between the as-built point cloud and the as-planned BIM model for construction progress monitoring.

Accurate 3D reconstructions of architecture, engineering, and construction AEC structures using UAV photogrammetry are often hindered by occlusions, excessive image overlaps, or insufficient coverage, leading to inefficient flight paths and extended mission durations. This work presents a BIM-aware, autonomous UAV trajectory generation framework wherein a compact, geometrically valid viewpoint network is first derived as a foundation for path planning. The network is optimized via Integer Linear Programming (ILP) to ensure coverage of IFC-modeled components while penalizing poor stereo geometry, GSD, and triangulation uncertainty. The resulting minimal network is then sequenced into a global path using a TSP solver and partitioned into battery-feasible epochs for operation on active construction sites. Evaluated on two synthetic and one real-world case study, the method produces autonomous UAV trajectories that are 31–63% more compact in camera usage, 17–35% shorter in path length, and 28–50% faster in execution time, without compromising coverage or reconstruction quality. The proposed integration of BIM modeling, ILP optimization, TSP sequencing, and endurance-aware partitioning enables the framework for digital-twin updates and QA/QC monitoring, accordingly, offering a unified, geometry-adaptive solution for autonomous UAV inspection and remote sensing.

UAV photogrammetry plays an essential role in applications such as disaster management, food security, and mapping due to its adaptability, scalability, and accuracy in data collection. Consequently, the increasing popularity of the UAV photogrammetry domain has led to a growing demand for comprehensive education accessible through academic institutions. Meanwhile, the flexibility and freedom associated with online education have sparked significant demand for online learning programs. Different users actively seek online courses and materials to delve into the intricacies of UAV photogrammetry. However, the abundance of online resources in this area poses challenges in finding such reliable courses.To address this challenge, the ITC team from the UAV centre and EOS Department at the University of Twente investigated the current online education courses related to UAV Photogrammetry offered globally. They compiled an inventory of these courses, categorizing them according to different educational levels- introductory, intermediate, and advanced—to align with the needs of key photogrammetry user categories. The project also investigated the currently available UAV datasets and software packages supporting technical training and professional applications. Further details are available on the project website.Besides, the project gathers insights into gaps in UAV photogrammetry knowledge areas and UAV applications in geoscience by designing, distributing, and analysing a UAV Gap survey.

Dams play a pivotal role in providing essential services such as energy generation, water supply, and flood control. However, their stability is crucial, and continuous monitoring is vital to mitigate potential risks. The Mosul Dam is one of the most interesting infrastructures in Iraq because it was constructed on alternating beds of karstified and gypsum which required continuous grouting due to water seepage. Therefore, the ongoing maintenance issues raised international concerns about its stability. For several years the dam indicated a potential for disastrous failure that could cause massive flooding downstream and pose a serious threat to millions of people. This research focuses on comprehensive statistical assessments of the dam geodetic network points across multiple epochs of long duration. Through the systematic application of three statistical tests and the predictive capabilities of the Kalman filter, safety and long-term stability are aimed to be enhanced. The analysis of the dam’s geodetic network points shows a consistent trend of upstream-to-downstream movement. The Kalman filter demonstrates promising outcomes for displacement prediction compared to least squares adjustment. This research provides valuable insights into dam stability assessment, aligns with established procedures, and contributes to the resilience and safety of critical infrastructure. The outcome of this paper can encourage future studies to build upon the foundation presented.

This review is aimed at exploring the use of remote sensing technology with a focus on Unmanned Aerial Vehicles (UAVs) in monitoring and management of palm pests and diseases with a special focus on date palms. It highlights the most common sensor types, ranging from passive sensors such as RGB, multispectral, hyperspectral, and thermal as well as active sensors such as light detection and ranging (LiDAR), expounding on their unique functions and gains as far as the detection of pest infestation and disease symptoms is concerned. Indices derived from UAV multispectral and hyperspectral sensors are used to assess their usefulness in vegetation health monitoring and plant physiological changes. Other UAVs are equipped with thermal sensors to identify water stress and temperature anomalies associated with the presence of pests and diseases. Furthermore, the review discusses how LiDAR technology can be used to capture detailed 3D canopy structures as well as volume changes that may occur during the progressing stages of a date palm infection. Besides, the paper examines how machine learning algorithms have been incorporated into remote sensing technologies to ensure high accuracy levels in detecting diseases or pests. This paper aims to present a comprehensive outline for future research focusing on modern methodologies, technological improvements, and direction for the efficient application of UAV-based remote sensing in managing palm tree pests and diseases.

Mapping systems using multi-beam LiDARs are widely used nowadays for different geospatial applications graduating from indoor projects to outdoor city-wide projects. These mobile mapping systems can be either ground-based or aerial-based systems and are mostly equipped with inertial navigation systems INS. The Velodyne HDL-32 LiDAR is a well-known 360° spinning multi-beam laser scanner that is widely used in outdoor and indoor mobile mapping systems. The performance of such LiDARs is an ongoing research topic which is quite important for the quality assurance and quality control topic. The performance of this LiDAR type is correlated to many factors either related to the device itself or the design of the mobile mapping system. Regarding design, most of the mapping systems are equipped with a single Velodyne HDL32 in a specific orientation angle which is different among the mapping systems manufacturers. The LiDAR orientation angle has a significant impact on the performance in terms of the density and coverage of the produced point clouds. Furthermore, during the lifetime of this multi-beam LiDAR, one or more beams may be defected and then either continue the production or returned to the manufacturer to be fixed which then cost time and money. In this paper, the design impact analysis of a mobile laser scanning (MLS) system equipped with a single Velodyne HDL-32E will be clarified and a clear relationship is given between the orientation angle of the LiDAR and the output density of points. The ideal angular orientation of a single Velodyne HDL-32E is found to be at 35° in a mobile mapping system. Furthermore, we investigated the degradation of points density when one of the 32 beams is defected and quantified the density loss percentage and to the best of our knowledge, this is not presented in literature before. It is found that a maximum of about 8% point density loss occurs on the ground and 4% on the facades when having a defected beam of the Velodyne HDL-32E.

3D reconstruction is a long-standing research topic in the photogrammetric and computer vision communities; although a plethora of open-source and commercial solutions for 3D reconstruction have been released in the last few years, several open challenges and limitations still exist. Undoubtedly, deep learning algorithms have demonstrated great potential in several remote sensing tasks, including image-based 3D reconstruction. State-of-the-art monocular and stereo algorithms leverage deep learning techniques and achieve increased performance in depth estimation and 3D reconstruction. However, one of the limitations of such methods is that they highly rely on large training sets that are often tedious to obtain; even when available, they typically refer to indoor, close-range scenarios and low-resolution images. Especially while considering UAV (Unmanned Aerial Vehicle) scenarios, such data are not available and domain adaptation is not a trivial challenge. To fill this gap, the UAV-based multi-sensor dataset for geospatial research (UseGeo - https://usegeo.fbk.eu/home) is introduced in this paper. It contains both image and LiDAR data and aims to support relevant research in photogrammetry and computer vision with a useful training set for both stereo and monocular 3D reconstruction algorithms. In this regard, the dataset provides ground truth data for both point clouds and depth maps. In addition, UseGeo can be also a valuable dataset for other tasks such as feature extraction and matching, aerial triangulation, or image and LiDAR co-registration. The paper introduces the UseGeo dataset and validates some state-of-the-art algorithms to assess their usability for both monocular and multi-view 3D reconstruction.

Geometric errors in LoD2 building models can be caused by the modeling algorithm but are often related to the quality of input data. One approach to tackling the modeling errors caused by the quality of input data is to collect additional data with a UAV and remodel the buildings. However, no flight planning approach exists specifically designed for efficient data recollection for model improvement. In this paper, we propose an innovative flight planning approach for this purpose. Contrary to the conventional method that recollects the data covering the entire building roof, our approach only collects the data over the erroneous region and uses it to improve the erroneous model part later. Our algorithm utilizes the existing LiDAR survey data to automatically detect model errors and design the camera networks by considering the roof geometry. We optimize the trajectory that connects the viewpoints with a genetic algorithm and develops an obstacle avoidance function with ray-casting to ensure a collision-free path. The proposed flight plan is implemented in a real-world scene. Our result shows an improved point cloud created through dense image matching with the collected UAV image data. The generated point cloud is successfully used for creating partial building models for improving the original models.

Camera network design is a challenging task for many applications in photogrammetry, biomedical engineering, robotics, and industrial metrology, among other fields. Many driving factors are found in the camera network design including the camera specifications, object of interest, and type of application. One of the interesting applications is 3D face modeling and recognition which involves recognizing an individual based on facial attributes derived from the constructed 3D model. Developers and researchers still face difficulty in reaching the required high level of accuracy and reliability needed for image-based 3D face models. This is caused among many factors by the hardware limitations and imperfection of the cameras and the lack of proficiency in designing the ideal camera-system configuration. Accordingly, for precise measurements, we still need engineering-based techniques to ascertain the specific level of deliverables quality. In this paper, an optimal geometric design methodology of the camera network is presented by investigating different multi-camera system configurations composed of four up to eight cameras. A mathematical nonlinear constrained optimization technique is applied to solve the problem and each camera system configuration is tested for a facial 3D model where a quality assessment is applied to conclude the best configuration. The optimal configuration is found to be a 7-camera array, comprising a pentagon shape enclosing two additional cameras, offering high accuracy. For those who prioritize point density, a 9-camera array with a pentagon and quadrilateral arrangement in the X-Z plane is a viable choice. However, a 5-camera array offers a balance between accuracy and the number of cameras.

The use of Unmanned Aerial Vehicles (UAVs) for surveying is at the forefront of their use in the Architectural Engineering and Construction (AEC) industry. UAVs make accessing hard-to-reach construction regions simpler and more cost-effective because of their small size, ease of mobility, and the wealth of information given by their integrated sensors. Accordingly, their use is thriving in different AEC sectors such as the management and inspection of engineering facilities such as concrete bridges. Overpass bridge engineering inspections are still applied using high accuracy surveying instruments in situ to ensure meeting the quality standards of construction. One important application is to measure the bridge pier caps centerline fitting using total stations, which is costly in terms of time and labor. Therefore, in this article, a new approach based on consumer-grade UAV imaging is proposed for replacing the traditional surveying techniques which are expected to improve automation and reduce time and cost. The proposed method utilized a sequence of processes on the UAV point clouds of the bridge concrete pier caps to finally extract the pier pads center and check their alignment. In two experiments, point clouds are created using DJI Phantom 3 images taken over bridge pier projects under construction, and concrete pad centers are then estimated and compared to the reference total station measurements. The results of both tests reveal the ability of the proposed method to attain the required accuracy for the pads’ alignment, as the root mean square error (RMSE) is one centimeter and two centimeters for the first and second tests, respectively. In addition, the new approach can reduce implementation time and the project budget.

Several advancements are going with Unmanned Aerial Systems (UAS) with the addition of multiple sensors and simultaneous data acquisition to obtain detailed geo-data for various applications. However, simultaneous data acquisition with multiple sensors, namely camera, and LiDAR, will also result in possible discrepancies associated with them, and they need to be solved to use a reliable and accurate final product. Several errors can be associated with both camera and LiDAR datasets due to the different characteristics of the sensors and terrain conditions. This research paper aimed to minimize the errors between LiDAR and the image datasets simultaneously acquired with an Unmanned Aerial System (UAS) by implementing a hybrid adjustment approach with a criterion for the roughness and threshold angle between surface normals. The initial trajectory of the UAS, raw LiDAR measurements, and image observations were the inputs used for the hybrid adjustment. The hybrid adjustment workflow minimizes the discrepancies with a least-squares-based simultaneous adjustment for both LiDAR and image datasets. For the hybrid adjustment process, three types of correspondences were established, namely: between image pairs, overlapping LiDAR strips, and between Image tie points and LiDAR strips. For quality control, mean Cloud-to-Cloud distances (C2C) were compared between both LiDAR and camera point clouds before and after hybrid adjustment. The surface-level analysis of the results was also carried out to analyze the errors before and after hybrid adjustment at a surface level for different types of surfaces. The results showed that the alignment between the point clouds has significantly improved from the range of meters to a centimeter-level after implementing the hybrid adjustment process. The proposed hybrid adjustment workflow can be used in mapping applications where a centimeter-level accuracy is requested.

Currently, there is a rapid trend in the production of airborne sensors consisting of multi-view cameras or hybrid sensors, i.e., a LiDAR scanner coupled with one or multiple cameras to enrich the data acquisition in terms of colors, texture, completeness of coverage, accuracy, etc. However, the current UAV hybrid systems are mainly equipped with a single camera that will not be sufficient to view the facades of buildings or other complex objects without having double flight paths with a defined oblique angle. This entails extensive flight planning, acquisition duration, extra costs, and data handling. In this paper, a multi-view camera system which is similar to the conventional Maltese cross configurations used in the standard aerial oblique camera systems is simulated. This proposed camera system is integrated with a multi-beam LiDAR to build an efficient UAV hybrid system. To design the low-cost UAV hybrid system, two types of cameras are investigated and proposed, namely the MAPIR Survey and the SenseFly SODA, integrated with a multi-beam digital Ouster OS1-32 LiDAR sensor. Two simulated UAV flight experiments are created with a dedicated methodology and processed with photogrammetric methods. The results show that with a flight speed of 5 m/s and an image overlap of 80/80, an average density of up to 1500 pts/m2 can be achieved with adequate facade coverage in one-pass flight strips.

Ongoing developments in video resolution either using consumer-grade or professional cameras has opened opportunities for different applications such as in sports events broadcasting and digital cinematography. In the field of geoinformation science and photogrammetry, image-based 3D city modeling is expected to benefit from this technology development. Highly detailed 3D point clouds with low noise are expected to be produced when using ultra high definition UHD videos (e.g., 4K, 8K). Furthermore, a greater benefit is expected when the UHD videos are captured from the air by consumer-grade or professional drones. To the best of our knowledge, no studies have been published to quantify the expected outputs when using UHD cameras in terms of 3D modeling and point cloud density. In this paper, a quantification is shown about the expected point clouds and orthophotos qualities when using UHD videos from consumer-grade drones and a review of which applications they can be applied in. The results show that an improvement in 3D models of ≅65% relative accuracy and ≅90% in point density can be attained when using 8K video frames compared with HD video frames which will open a wide range of applications and business cases in the near future.

Heritage at risk is a terminology used to describe the sites that are highly at risk of being lost as a result of intentional demolition, deterioration, negligence or subject to improper preservation or mistreatment. Iraq is one of the countries that suffered in the last decade from intentional demolition of highly valuable heritage sites and objects. As Iraq gradually recovering from wars and violence with limited resources and budgets, historical and heritage places are still at risk because of neglect, community ignorance, insufficient planning, and military actions. Therefore, in this paper, we propose the idea of using crowdsource drone images and videos which are captured by amateurs for the documentation of heritage sites. Those crowdsource images represent a great source of data that does not require significant financial and hard labor resources. It should be noted that there is no guarantee to have the captured data being sufficient for the 3D documentation and therefore it is proposed to integrate, when possible, multiple captured crowdsource data to ensure complete documentation. In this paper, three Iraqi historical sites are 3D reconstructed using crowdsource drone videos, namely: Rabban Hormizd Monastery (AD 640), Taq Kasra (AD 242 to 272), and the Great Mosque of Samarra (AD 849-851). The experiments showed a successful 3D modeling of the three mentioned heritage objects using the crowdsource drone video images despite being captured for non-3D purposes which require high expertise and planning. With the absence of highly accurate reference data, the overall relative accuracy of the object's dimensions is found to be less than 1 m.