Rockfalls are natural slope-instability phenomena that pose a significant hazard to infrastructure and human activity. In recent years, the increasing availability of high-resolution three-dimensional (3D) models acquired through photogrammetric techniques has enabled detailed pre-/post-event analyses of rock slopes. However, in this domain, the availability of accurate ground truth for the quantitative evaluation of 3D change detection methods and for training machine-learning approaches aimed at recognising and volumetrically quantifying detachments on rock faces remains very limited. This work presents a simulator that, starting from a 3D model of a rock face, generates pre-/post-failure scenarios through controlled removal of rock blocks and produces photogrammetric acquisitions affected by realistic measurement noise. The pipeline emulates the main processing stages of the reconstruction workflow. Noise realism is validated and calibrated by comparing real and simulated data through a multi-indicator framework (marginal distribution, variogram, power spectrum, and multiscale roughness), integrated into a Mahalanobis-distance-based acceptance test with empirical thresholds derived from real measurements. Results from two pilot sites show that, after site-specific tuning of the simulator noise levels, the calibrated configurations reproduce the main magnitude and spatial-structure characteristics of the real noise, with stronger agreement for the fixed stereo-pair configuration and partial but still informative agreement for the more complex UAV-based case. Moreover, the simulator provides a controlled environment for benchmarking and sensitivity analyses of change detection methods.

Abstract. This article addresses the challenges of conducting integrated 3D surveys of complex historic architecture, focusing on the documentation and modelling of the Basilica of Santa Maria della Steccata in Parma (Italy). The Basilica’s impressive scale, rich decorative features, and complex architectural layout – including a network of secondary spaces and attics accessible only through narrow, meandering paths – posed significant challenges for the survey. To overcome these obstacles, an integrated approach combining Terrestrial Laser Scanning (TLS), Close-Range Photogrammetry (CRP), Spherical Photogrammetry (SP), and UAV Photogrammetry was employed. The article outlines the planning, execution, and processing phases of the survey campaign, with particular emphasis on the methodological issues involved in merging data from multiple sources in such a constrained and heterogeneous environment. In this context, the article introduces and evaluates an ICP-assisted Bundle Block Adjustment (ICP-BBA) strategy designed to improve CRP- and TLS-derived models, demonstrating its effectiveness in enhancing local consistency in areas prone to residual misalignments. In addition, the performance of SP is examined under varying spatial conditions, highlighting its potential both as a supplementary method for areas with restricted accessibility and as a stand-alone alternative in specific use cases.

This study compares the photogrammetric performance of three multi-camera systems—two spherical cameras (INSTA 360 Pro2 and MG1) and one multi-camera rig (ANT3D)—to evaluate their accuracy and precision in confined environments. These systems are particularly suited for indoor surveys, such as narrow spaces, where traditional methods face limitations. The instruments were tested for the survey of a narrow spiral staircase within Milan Cathedral and the results were analyzed based on different processing strategies, including different relative constraints between sensors, various calibration sets for distortion parameters, interior orientation (IO), and relative orientation (RO), as well as two different ground control solutions. This study also included a repeatability test. The findings showed that, with appropriate ground control, all systems achieved the target accuracy of 1 cm. In partially unconstrained scenarios, the drift errors ranged between 5 and 10 cm. Performance varied depending on the processing pipelines; however, the results suggest that imposing a multi-camera constraint between sensors and estimating both IO and RO parameters during the Bundle Block Adjustment yields the best outcomes. In less stable environments, it might be preferable to pre-calibrate and fix the IO parameters.

Multi-camera devices are increasingly popular in various metrological applications, including cultural heritage digitalisation, where these devices are adopted as low-cost alternatives to more traditional methods or mobile mapping systems. They can be of two types: panoramic and non-panoramic configurations, with the former usually more compact and ready-made off-the-shelves and the latter usually custom-developed for metrological applications. In the paper, we compare the accuracy and reliability performance of two types of multi-camera: the spherical camera INSTA 360 Pro2 and the custom multi-camera rig Ant3D. The case study is a challenging spiral staircase environment, typical in many cultural heritage survey projects. The processed image datasets were evaluated in the most common constrain scenario (GCPs at both ends of the staircase) and the worst-case scenario (open-ended path, GCPs at the start). The datasets were processed with precalibrated IO and various degrees of multi-camera constraints up to precalibrated relative orientations. The results highlight that the nominal scale 1:50 can be achieved, e.g. an accuracy of <2 cm plus complete and precise point clouds and mesh results.

This work focuses on investigating the accuracy of 3D reconstructions from fixed stereo-photogrammetric monitoring systems through different camera calibration procedures. New reliable and effective calibration methodologies that require minimal effort and resources are presented. A full-format camera equipped with fixed 50 and 85 mm focal length optics is considered, but the methodologies are general and can be applied to other systems. Four different calibration strategies are considered: (i) full-field calibration (FF); (ii) multi-image on-the-job calibration (MI); (iii) point cloud-based calibration (PC); and (iv) self (on-the-job) calibration (SC). To evaluate the calibration strategies and assess their actual performance and practicality, two test sites are used. The full-field calibration, while very reliable, demands significant effort if it needs to be repeated. The multi-image strategy emerges as a favourable compromise, offering good results with minimal effort for its realisation. The point cloud-based method stands out as the optimal choice, balancing ease of implementation with quality results; however, it requires a reference 3D point cloud model. On-the-job calibration with monitoring images is the simplest but least reliable option, prone to uncertainty and potential inaccuracies, and should hence be avoided. Ultimately, prioritising result reliability over absolute accuracy is paramount in continuous monitoring systems.

Cold mix patching materials (CMPMs) have become increasingly popular solutions for the emergency asphalt repairs of small- to medium-sized potholes in severe winter conditions or where short reopening times are required. Their performances need to be carefully monitored in the early post-application phases, when these materials have a high potential to face several distresses. The research aimed to identify a set of methods for the rapid and cost-effective acquisition of asphalt pavement surface geometric data to analyze the effectiveness of a given CMPM, with particular attention to procedures that minimize interference with the normal traffic flow or limit the lanes closure whilst ensuring the safety of operators and pedestrians. A road trial section in a sub-urban industrial zone, where ideal potholes were cut and filled with different types of CMPMs, was set up for this purpose. A low-cost unmanned aerial vehicle (UAV), was used for the experiments, repeating the drone survey in four successive epochs (up to 30 days). The captured images were post-processed with a photogrammetric software and the co-registered point clouds at the different epochs were compared to highlight the early patches performances and deteriorations. This ready-to-use methodology, tailored on the urban scale, made it possible to identify over time the occurrence of raveling on the patch area and to monitor the trend of patch surface depressions, extracting desired transverse and longitudinal profiles.

This study presents low-cost techniques for monitoring soil erosion on mountain trails within the context of the HUMANITA project, which focuses on mitigating the environmental impacts from recreational activities in protected areas. Monitoring erosion in mountain environments poses several challenges that must be considered to select optimal techniques, such as limited accessibility, instrument portability, achievable level of detail, absence of data connectivity and Ground Control Point establishment. In addition, soil erosion is a widespread issue that requires surveys over large areas and must be repeated periodically to ensure accurate assessment and track changes over time. Consequently, the cost and ease of use of surveying equipment are critical.Six protected areas in Italy and Central Europe were selected as pilot sites. Three scenarios were explored, each characterized by different spatial extents and level of details required for erosion assessment: detailed analysis of small areas (scenario 1), narrow forest trails (scenario 2), and broad open areas (scenario 3). Scenario 1 employed high-precision techniques such as Terrestrial Laser Scanning and close-range photogrammetry to capture micro-scale changes. Scenario 2 utilized spherical photogrammetry and UAVs to survey narrow, vegetated trails with high resolution and accuracy. Scenario 3 focused on UAV photogrammetry for monitoring large areas. Key challenges included multi-epoch data co-registration, establishing stable ground control points, and ensuring and assessing surveys repeatability. The results highlight the capabilities, limitations, and cost-effectiveness of these geomatics techniques, providing practical guidelines for sustainable trail management and erosion monitoring in protected mountain areas.

Nowadays, the use of carbon-neutral materials is an urgent need because of energy consumption and CO2-emissions concerns. Thus, the use of ladle furnace steel slags (LFSs) is becoming common in asphalt pavement fields. One of the limitations on using LFSs is linked to swelling potential because of the changes in chemical structure due to the hydration of their components. In this study, two different asphalt binders, neat and 3.5 % styrene-butadiene-styrene modified, were mixed with limestone coarse aggregates and two fillers (limestone and LFSs) to analyze the swelling potential and the performance levels of hot mix asphalts containing not hydrated LFSs as filler. The analysis was conducted performing the SuperPave indirect tensile test protocol at 10 & DEG;C and by using a 3D-digital image correlation metadata model (3D-DICM) capable of computing the volumetric expansion of materials after four different periods of conditioning in water (24, 48, 168, and 336 h). The results showed no significant differences among the analyzed materials, highlighting that the volumetric expansion is mainly linked to the aggregates' water absorption. On the other hand, conditioning time seems to affect the deformability of the mixtures, influencing performance level.

This paper presents a low-cost framework for creating urban digital twins in Virtual Reality (VR) tailored for heritage preservation and smart city applications. The increasing demand for urban digital twins necessitates an integration of diverse data sources to enhance urban management, particularly in historical contexts where traditional methods may lack necessary specificity. To address this, our research integrates advanced 3D survey techniques (mostly low-cost) with publicly available datasets to develop a semantically rich, detailed urban digital model aligned with specific requirements of each unique urban setting.The methodology hinges on three pivotal stages: data acquisition, data management, and data accessibility. Data acquisition involves collecting extensive data both from existing datasets and 3D surveys, emphasizing on identifying optimal, cost-effective solutions suited to the surveyed area. Data management is achieved using a broker database coupled with a Web Application Programming Interface (Web API), ensuring the integrity of original databases while enabling flexible system implementation. Data accessibility extends to a broad range of applications, including GIS, BIM, and customized applications, enhancing the scalability of the digital twin model.The test ground of this system is a VR application developed with Unity, which serves as the interactive platform for the digital twin model. The proposed framework is validated through three case studies in distinct urban settings, each chosen to illustrate the framework's adaptability, versatility, and effectiveness in different urban complexities. The results demonstrate the potential of the digital twin model in facilitating detailed urban management tasks, promoting sustainable heritage conservation, and fostering smarter urban environments.

Accurate building extraction holds paramount importance in various applications such as urbanization rate calculations, urban planning, and resource allocation. In response to the escalating demand for precise low-altitude unmanned aerial vehicle (UAV) building segmentation in intricate scenarios, this study introduces a semi-supervised methodology to alleviate the labor-intensive process of procuring pixel-level annotations. Within the framework of adversarial networks, we employ a dual-channel parallel generator strategy that amalgamates the morphology-driven optical flow estimation channel with an enhanced multilayer sensing Deeplabv3+ module. This approach aims to comprehensively capture both the morphological attributes and textural intricacies of buildings while mitigating the dependency on annotated data. To further enhance the network’s capability to discern building features, we introduce an adaptive attention mechanism via a feature fusion module. Additionally, we implement a composite loss function to augment the model’s sensitivity to building structures. Across two distinct low-altitude UAV datasets within the domain of UAV-based building segmentation, our proposed method achieves average mean pixel intersection-over-union (mIoU) ratios of 82.69% and 79.37%, respectively, with unlabeled data constituting 70% of the overall dataset. These outcomes signify noteworthy advancements compared with contemporaneous networks, underscoring the robustness of our approach in tackling intricate building segmentation challenges in the domain of UAV-based architectural analysis.

Though with a less dramatic growth compared to photogrammetry, remote sensing from multispectral imagery taken by UAV (Unmanned Aerial Vehicles) platforms is applied to vegetation health monitoring, crop management, water quality assessments, geological inspections and much more, with a sizeable number of multispectral cameras now available on the market. As for satellite images, a key point in remote sensing is calibration, both geometric and radiometric, and the modelling of disturbances, to get well co-registered reflectance data. Leaving aside radiometric calibration, this paper focuses on how to best georeference the different bands one with respect to the other. This is normally achieved by the so-called band-to-band registration (BBR). Here, a straightforward approach is proposed, that exploits the multi-camera geometry and, unlike BBR, all the information contents of the bands, as inter-band matches are searched (and possibly found) for every pair of bands and not only between a reference and a slave band. Tests on images taken with the 9-band MAIA S2 camera are presented, discussing the pro and cons of pre-calibration and on-the-job calibration of the camera parameters of each sensor. The results found show that the proposed method is at least as good as the BBR ones.

Digital photogrammetry is a widespread surveying technique in different fields of application due to its flexibility, versatility and cost-effectiveness. Despite its increasing automation and simplicity, a proper image block design is crucial to ensure high standards of performance and accuracy. Studies on camera network design have been largely dealt with in the scientific literature with reference to image orientation process, while they are still poor on dense matching. This paper investigates the influence of different block geometry configurations on multi-image dense matching. Starting from the same orientation solution, dense matching was performed considering different combinations of number of images and base length distance between the first and the last image within a strip. The raster Digital Elevation Models (DEM) resulting from each sequence of images were compared with a reference DEM to assess accuracy and completeness. The tests were conducted using different cameras and at various test sites to assess different survey conditions and generalize the findings. The presented results provide some operational guidance on block geometry optimization to maximize the accuracy and completeness.

A significant portion not yet investigated in the Regio V of the ancient city of Pompeii is currently the subject of the most remarkable excavation in the post-World War II period. These activities relate to a triangular area, the so-called "wedge", which is placed between the houses of Nozze d'Argento and Marco Lucrezio Frontone. Besides the astonishment and scientific emotions resulting from the finding of magnificent frescoed and well preserved thennopolium, dwellings and balconies, also a feeling of extreme interest in the pavement engineering arose due to a further one of a kind discovery. The Somma-Vesuvium volcanic eruption of 79 AD buried a narrow alley, i.e. vicolo dei Balconi, during the construction of its stone pavement, in which the laying of compact lava stone flags was interrupted about halfway from north to south. The eruption and the following superimposition of lapilli, ash and pumice blankets has therefore maintained in a state of perfect documental conservation the whole area, preserving the new laid flagstones still showing the original surface finishing and the preparatory subgrade. Thus, this historical and archeological source becomes a priceless engineering evidence describing some of the design and construction techniques used by the Romans at Pompeii. The excavated area has been analyzed following a cross-disciplinary approach, adopting several minimally intrusive or non-destructive techniques and instruments belonging to different branches of science to capture some specific aspects. Specifically, the alley has been initially surveyed with a terrestrial laser scanner (TLS) device to build an accurate layout model of the worksite. Although the TLS survey provided an invaluable source of information for the street structures documentation, specific stone paving elements were also analyzed using close range photogrammetry techniques and their friction properties have been measured exploiting the potential of a modern road engineering device, i.e. a skid resistance tester, also known as the British pendulum tester (BPT). The consistency analysis of the subgrade structure and homogeneity in the unpaved section involved the use of two dynamic procedures, i.e. a penetrometric (dynamic cone penetrometer - DCP) and a deflectometric (light weight deflectometer - LWD) test. Besides, some design and urban planning considerations have been drawn from the analysis of the relationships between the finished structures and the pavement construction site according to the lines of the existing curbs, the thresholds of access to the dwellings and the placement of enigmatic prismatic blocks arranged in trenches, probably intended as a useful target points for the stonemasons or temporary pedestrian passage. In a wider reading a succession of paving works (and not repaving) using stone elements in this part of the city starting from the Cardo via del Vesuvio (from west to east) is recognizable. (C) 2022 Elsevier Masson SAS. All rights reserved.

The integration of photogrammetry and Terrestrial Laser Scanner (TLS) techniques is often desirable for Cultural Heritage digitization, especially when high metric and radiometric accuracy is required, as for the documentation and restoration of frescoed spaces. Despite the many technological and methodological advances in both techniques, their full integration is still not straightforward. The paper investigates a methodology where TLS and photogrammetric data are processed together through an image matching process between RGB panoramas acquired by the scanner’s integrated camera and frame imagery acquired through photographic equipment. The co-registration is performed without any Ground Control Point (GCP) but using the automatically extracted tie points and the known Exterior Orientation parameters of the panoramas (gathered from TLS data original registration) to set the ground reference. The procedure allowed for effective integrated processing with the possibility of take benefit from TLS and photogrammetry pros and demonstrated to be reliable even with low overlap between photogrammetric images.

Traditionally, data co-registration of survey epochs in photogrammetry relied on Ground Control Points (GCP) to keep the reference system unchanged. In the last years, Unmanned Aerial Systems (UAV) are increasingly used in photogrammetric environmental monitoring. The diffusion of affordable UAV platforms equipped with GNSS (Global Navigation Satellite System) centimetre-grade receivers might reduce, but not eliminate, the need for GCP. Conversely, if GNSS-assisted orientation cannot be used or if additional ground control and reliability checks are required, alternatives to repeated GCP survey have been proposed, taking advantage of Structure from Motion (SfM) photogrammetry. In particular, co-registering different epochs image blocks together, identifying corresponding features, has been demonstrated as a viable and efficient approach. In this paper four different strategies easily implementable in a generic commercial photogrammetric software are presented and compared considering three different test sites in Italy subject to different amounts of environmental changes. The influence of the amount and distribution of inter-epoch corresponding points on the accuracy of the reconstruction is investigated. The results show that some of the tested strategies obtains very good results and can be used (although not needed) also in RTK centimetre-grade UAV surveys, leveraging the additional information coming from previous epochs survey to actually increase the survey accuracy and reliability.

A dome-shape deformation has been found to affect the photogrammetric surface reconstruction in several real and simulated experiments. Its origin has been recognised in inaccurate estimation of the camera parameters and many papers already concentrated on conditions to avoid its development, especially as far as block design is concerned. This paper presents a Monte Carlo simulation to investigate surface reconstruction elevation errors in UAV (Unmanned Aerial Vehicle) photogrammetric blocks. The simulation tests are designed to find out the effect of block shape, camera axis inclination, side-lap, cross strips addition and block control by GCP or GNSS-assisted on the extent of the deformations. The main findings are: i) that GNSS-assisted blocks are generally more robust compared to GCP-controlled ones; ii) that, in GNSS-assisted blocks, unless a mix of nadiral and inclined strips is present, at least one fixed GCP must be provided; iii) that cross strip can conveniently be slimmed to save flight time and processing time; iv) that the effectiveness of GNSS deteriorate as the block shape slims out.

Acknowledged guidelines and standards such as those formerly governing project planning in analogue aerial photogrammetry are still missing in UAV photogrammetry. The reasons are many, from a great variety of projects goals to the number of parameters involved: camera features, flight plan design, block control and georeferencing options, Structure from Motion settings, etc. Above all, perhaps, stands camera calibration with the alternative between pre- and on-the-job approaches. In this paper we present a Monte Carlo simulation study where the accuracy estimation of camera parameters and tie points' ground coordinates is evaluated as a function of various project parameters. A set of UAV (Unmanned Aerial Vehicle) synthetic photogrammetric blocks, built by varying terrain shape, surveyed area shape, block control (ground and aerial), strip type (longitudinal, cross and oblique), image observation and control data precision has been synthetically generated, overall considering 144 combinations in on-the-job self-calibration. Bias in ground coordinates (dome effect) due to inaccurate pre-calibration has also been investigated. Under the test scenario, the accuracy gap between different block configurations can be close to an order of magnitude. Oblique imaging is confirmed as key requisite in flat terrain, while ground control density is not. Aerial control by accurate camera station positions is overall more accurate and efficient than GCP in flat terrain.

Digital surface models (DSM) have become one of the main sources of geometrical information for a broad range of applications. Image-based systems typically rely on passive sensors which can represent a strong limitation in several survey activities (e.g., night-time monitoring, underground survey and night surveillance). However, recent progresses in sensor technology allow very high sensitivity which drastically improves low-light image quality by applying innovative noise reduction techniques. This work focuses on the performances of night-time photogrammetric systems devoted to the monitoring of rock slopes. The study investigates the application of different camera settings and their reliability to produce accurate DSM. A total of 672 stereo-pairs acquired with high-sensitivity cameras (Nikon D800 and D810) at three different testing sites were considered. The dataset includes different camera configurations (ISO speed, shutter speed, aperture and image under-/over-exposure). The use of image quality assessment (IQA) methods to evaluate the quality of the images prior to the 3D reconstruction is investigated. The results show that modern high-sensitivity cameras allow the reconstruction of accurate DSM in an extreme low-light environment and, exploiting the correct camera setup, achieving comparable results to daylight acquisitions. This makes imaging sensors extremely versatile for monitoring applications at generally low costs.

The paper investigates the influence of lighting conditions on image-based 3D surface reconstruction, with particular focus on periodic photogrammetric surveys for monitoring and 3D mapping applications. The analyses focus on the accuracy and completeness of each DSM and the daily and hourly repeatability of repeated photogrammetric surveys. Three test sites with rock slopes with a different orientation to the sun and different slope characteristics (slope, pattern, amount of outcropping elements that cast shadows) have been considered to ensure that results can give a general indication of the behaviours in different light conditions. In addition, a simulated virtual test site is included in the study to allow controlled image acquisition and evaluate the effect of the sun’s inclination on the DSM accuracy without influence of other weather conditions. The results show that, although there is an optimal time for the acquisitions, if particularly unfavourable light conditions are excluded, the accuracy reduction with time variation is always below 30%. The repeatability analyses by day and by time highlight a good consistence between DEMs belonging to the same day but acquired at different times and, also, between DEMs acquired at the same time but on different days. This suggests that reliable results can be obtained during continuous monitoring of, for instance, rock faces to identify rockfalls.

Pavement management system (PMS) is a set of tools that assist road agencies in finding optimal strategies for maintaining pavements in a serviceable condition over a period of time. Usually, municipalities base their PMS on the deterioration monitoring through a visual survey but the distresses identification is complex and the operations are based on visual and instrumental inspections. As regards natural stone pavements, which are very widespread in the road heritage of cities, in literature there are very few studies. The authors analyzed two supervised classification approaches (Semi-Automatic Classification Plugin for QGIS and a Convolutional Neural Network (CNN)), based on Unmanned Aerial Vehicle (UAV) photogrammetry, to detect stone pavement's pattern. This study showed that using a U-Net CNN on images obtained from UAV is an excellent alternative to the traditional manual inspection and can be implemented for other types of stone pavements, also with the aim of distress identification.