Photogrammetric Workflow Research Articles

Multi-approach study, digitization and dissemination of a Bronze-Age engraved cup found in Filo Braccio, Filicudi (Aeolian Islands, Italy)

This paper presents a multidisciplinary study combining photogrammetry, near-infrared (NIR) imaging and archaeological analysis to analyse a 1900-1800 BC engraved cup, found at the Bronze Age site of Filo Braccio in Filicudi, Aeolian Islands, Italy. The artefact is unique within the contemporary ‘Capo Graziano’ culture, featuring a rare complex figural scene engraved along the exterior walls; the “scene” provides insights into the prehistoric culture of Filicudi and the Aeolian Islands. The study focused on generating an accurate three-dimensional (3D) model to i) support archaeological research on the artefact's engravings and ii) create engaging digital media for remote and on-site visitors. Photogrammetry used high-resolution photographs taken around the object and control points for metric accuracy assessment. This study also utilises NIR and visible light imaging to examine the engraved cup. The photogrammetric workflow provided a realistic 3D model textured with both visible and NIR data: the 3D model enabled to improve the reading of the engraved scene, revealing horizontal registers of figures, while NIR imaging highlighted material inhomogeneity. The resulting 3D model achieved a high level of detail, with 4381407 faces and a root mean square (RMS) reprojection error of approximately 3.9 µm. The NIR imaging revealed additional surface details not visible in the standard photographs. For dissemination, the optimised 3D model was uploaded to Sketchfab with informative annotations, enabling remote study and cultural promotion of the artefact. This multi-approach methodology offers a valuable tool for comprehensive artefact documentation and analysis, providing new insights into the artefact's complex figural scene.

Novel View Synthesis with Neural Radiance Fields for Industrial Robot Applications

Abstract. Neural Radiance Fields (NeRFs) have become a rapidly growing research field with the potential to revolutionize typical photogrammetric workflows, such as those used for 3D scene reconstruction. As input, NeRFs require multi-view images with corresponding camera poses as well as the interior orientation. In the typical NeRF workflow, the camera poses and the interior orientation are estimated in advance with Structure from Motion (SfM). But the quality of the resulting novel views, which depends on different parameters such as the number and distribution of available images, the accuracy of the related camera poses and interior orientation, but also the reflection characteristics of the depicted scene, is difficult to predict. In addition, SfM is a time-consuming pre-processing step, and its robustness and quality strongly depend on the image content. Furthermore, the undefined scaling factor of SfM hinders subsequent steps in which metric information is required. In this paper, we evaluate the potential of NeRFs for industrial robot applications. To start with, we propose an alternative to SfM pre-processing: we capture the input images with a calibrated camera that is attached to the end effector of an industrial robot and determine accurate camera poses with metric scale based on the robot kinematics. We then investigate the quality of the novel views by comparing them to ground truth, and by computing an internal quality measure based on ensemble methods. For evaluation purposes, we acquire multiple datasets that pose challenges for reconstruction typical of industrial applications, like reflective objects, poor texture, and fine structures. We show that the robot-based pose determination reaches similar accuracy as SfM in non-demanding cases, while having clear advantages in more challenging scenarios. We also report results of the novel view quality for different NeRF approaches, showing that an additional online pose refinement may be disadvantageous. Finally, we present first results of applying the ensemble method to estimate the quality of the synthetic novel view in the absence of a ground truth.

Key-Point-Descriptor-Based Image Quality Evaluation in Photogrammetry Workflows

Photogrammetry depends critically on the quality of the images used to reconstruct accurate and detailed 3D models. Selection of high-quality images not only improves the accuracy and resolution of the resulting 3D models, but also contributes to the efficiency of the photogrammetric process by reducing data redundancy and computational demands. This study presents a novel approach to image quality evaluation tailored for photogrammetric applications that uses the key point descriptors typically encountered in image matching. Using a LightGBM ranker model, this research evaluates the effectiveness of key point descriptors such as SIFT, SURF, BRISK, ORB, KAZE, FREAK, and SuperPoint in predicting image quality. These descriptors are evaluated for their ability to indicate image quality based on the image patterns they capture. Experiments conducted on various publicly available image datasets show that descriptor-based methods outperform traditional no-reference image quality metrics such as BRISQUE, NIQE, PIQE, and BIQAA and a simple sharpness-based image quality evaluation method. The experimental results highlight the potential of using key-point-descriptor-based image quality evaluation methods to improve the photogrammetric workflow by selecting high-quality images for 3D modeling.

Evaluating error sources to improve precision in the co-registration of underwater 3D models

Change detection is an essential and widely used approach for investigating ecosystem dynamics. Multi-temporal 3D models increasingly underpin photogrammetry-based analyses of change for many ecologically relevant attributes. To detect change, it is necessary to accurately align 3D models collected at different times using a process referred to as co-registration. However, achieving precise co-registration is difficult in underwater habitats due to practical challenges intrinsic to surveying them. These include a lack of accurate georeferencing information, variable light, turbidity and weather conditions, and diving restrictions dictated by the diver's pressure exposure over time. Here we present an efficient co-registration workflow for 3D models that directly addresses these challenges, derived from underwater structure-from-motion methods. To test our approach, we used 3D models from across a wide range of coral reef habitats covering all those that one may encounter in shallow reefs (15 m depth and above). We then identified and empirically estimated four key sources of error: co-registration, 3D processing, image acquisition, and reference and scaling features (RSF) placement, and quantified their relative contributions to the overall error. Our proposed co-registration workflow had a mean precision of 1.37 ± 16.55 mm. Image acquisition and RSF placement errors contributed the most to the total workflow error (37% and 53%, respectively), while the contribution of co-registration and 3D processing errors was minimal (3% and 7%, respectively). As a result of our analysis, we provide ‘good practice’ guidelines to reduce errors associated with photogrammetric workflows and to facilitate efficient and reliable detection of 3D change in complex underwater ecosystems.

The Importance of Ground Control Points In A Photogrammetric Workflow

Abstract This study investigates the optimal distribution and pattern of ground control points (GCPs) in aerial photogrammetric projects. Aerial triangulation (AT), also known as bundle adjustment, is the fundamental step in refining 3D reconstruction models and camera positions, thereby minimizing reprojection errors. The study utilizes data from a national project in Romania, employing high-resolution aerial images acquisition using photogrammetric sensors. The project has rigorous requirements of ground control points (GCP) placement and field measurements using GNSS and geometric leveling techniques. The study employs various scenarios, manipulating the number and distribution of GCPs, to assess their influence on planimetric and altimetric accuracy. Results indicate that the configuration and number of GCPs significantly affect the accuracy of photogrammetric products, such as dense image point clouds, digital surface models, and orthophotos. Moreover, the study underscores the importance of precise GCP determination methods, especially in regions lacking a precise gravimetric geoid model. In scenarios with inadequate GCP coverage the outcomes have inferior quality, emphasizing the critical role of GCPs in ensuring the quality of photogrammetric products. Overall, the research gives a clear view on the best placement patterns of GCPs and their influence on AT process evaluation performed in check points (CHKs).

EVALUATION OF NERF 3D RECONSTRUCTION FOR ROCK ART DOCUMENTATION

Abstract. Digital documentation of rock art traditionally relies on a point cloud captured by a terrestrial laser scanner (TLS) or derived from an oriented image obtained using photogrammetry. In modern photogrammetry, the dense point cloud is generated using multi-view stereo (MVS) and subsequently used to generate a photorealistic 3D model. A recent method to reconstruct 3D models from images is Neural Radiance Fields (NeRF), which uses volume density to render the scenes through neural networks. The advantage of NeRF is that it can construct 3D models faster without using high computer processors and memory. NeRF has been studied in various applications, including cultural heritage, but not specifically for rock art documentation. Therefore, this paper evaluates three-dimensional (3D) reconstruction techniques using NeRF on Nerfstudio platform on two rock art datasets and compares them with the point cloud and 3D mesh models obtained from TLS and photogrammetry/MVS. The results have shown that NeRF does not match MVS in achieving geometric precision and texture quality. However, its learning-based approach accelerates reconstruction and offers potential enhancements to complement photogrammetric workflow.

Semi-automatic 3D crack map generation and width evaluation for structural monitoring of reinforced concrete structures

Bridge inspection is a time-consuming, expensive, but indispensable task. In this work, a new semi-automatic workflow for a concrete bridge condition assessment system is developed and discussed. The workflow consists of three main parts merged in the new methodology. The elements are the data acquisition with cameras, the automated damage detection and localization using a neural network, and the resulting engineering condition assessment. Furthermore, a CAD model serves as a base for the later calculations for the condition assessment. Camera images are used for both sub-millimeter crack detection using semantic segmentation by an artificial neural network and a crack localization based on a combination of a photogrammetric workflow including structure from motion (SfM) and the projection as imprinted points directly onto the as-planned CAD mesh. Moreover, an approach for crack width derivation is given. The captured crack width, crack position, and the date of detection represent the input values for subsequent crack monitoring. Thereby, this new concept is proposed as an essential step towards a time-efficient and objective life-cycle assessment of reinforced concrete structures.

Spatially continuous snow depth mapping by aeroplane photogrammetry for annual peak of winter from 2017 to 2021 in open areas

Abstract. Information on snow depth and its spatial distribution is important for numerous applications, including natural hazard management, snow water equivalent estimation for hydropower, the study of the distribution and evolution of flora and fauna, and the validation of snow hydrological models. Due to its heterogeneity and complexity, specific remote sensing tools are required to accurately map the snow depth distribution in Alpine terrain. To cover large areas (>100 km2), airborne laser scanning (ALS) or aerial photogrammetry with large-format cameras is needed. While both systems require piloted aircraft for data acquisition, ALS is typically more expensive than photogrammetry but yields better results in forested terrain. While photogrammetry is slightly cheaper, it is limited due to its dependency on favourable acquisition conditions (weather, light conditions). In this study, we present photogrammetrically processed high-spatial-resolution (0.5 m) annual snow depth maps, recorded during the peak of winter over a 5-year period under different acquisition conditions over a study area around Davos, Switzerland. Compared to previously carried out studies, using the Vexcel UltraCam Eagle Mark 3 (M3) sensor improves the average ground sampling distance to 0.1 m at similar flight altitudes above ground. This allows for very detailed snow depth maps in open areas, calculated by subtracting a snow-off digital terrain model (DTM, acquired with ALS) from the snow-on digital surface models (DSMs) processed from the airborne imagery. Despite challenging acquisition conditions during the recording of the UltraCam images (clouds, shaded areas and fresh snow), 99 % of unforested areas were successfully photogrammetrically reconstructed. We applied masks (high vegetation, settlements, water, glaciers) to increase the reliability of the snow depth calculations. An extensive accuracy assessment was carried out using check points, the comparison to DSMs derived from unpiloted aerial systems and the comparison of snow-free DSM pixels to the ALS DTM. The results show a root mean square error of approximately 0.25 m for the UltraCam X and 0.15 m for the successor, the UltraCam Eagle M3. We developed a consistent and reliable photogrammetric workflow for accurate snow depth distribution mapping over large regions, capable of analysing snow distribution in complex terrain. This enables more detailed investigations on seasonal snow dynamics and can be used for numerous applications related to snow depth distribution, as well as serving as a ground reference for new modelling approaches and satellite-based snow depth mapping.

LESSONS LEARNT FROM THE HIGH RESOLUTION UAS PHOTOGRAMMETRIC SURVEY OF A HISTORIC URBAN AREA: UNESCO SITE OF SABBIONETA

Abstract. In view of the increasing development of the smart city concept and the generation of 3d city models, it becomes essential to have an up-to-date, high-detail 3D survey of urban areas. There are several technologies that enable the development of a geometric survey of urban areas, including the use of aerial laser scanning, remote sensing, mobile mapping systems, and Unmanned Aerial Sistems (UAS) photogrammetry. Of these, the last mentioned, when developed with high-resolution cameras and flight plans with appropriate elevations, allows point clouds to be obtained at a high level of detail and orthophotos with great resolution. This technique may be the preferable choice when the object of the survey is a historic urban area, which has some special features, which might make surveying difficult with other surveying techniques. This paper presents the survey with UAS photogrammetry of a historic urban area: the city of Sabbioneta, in northern Italy. The UAS flight planning is discussed in details, specifically referring to european UAS flight regulations. The survey was developed with DJI Matrice 300 RTK, equipped with a flight terminator and parachute, and coupled with a high precision GNSS Mobile Station DJI D-RTK 2. Ground control points and check points were measured with GNSS receiver Leica GS18. Images were processed with Agisoft Metashape following a photogrammetric workflow. The resulting orthophoto has a pixel size of 1 cm. The obtained dense point cloud is suitable for future use for its segmentation by testing existing machine learning and deep learning methods and for future urban analysis.

360° IMAGE ORIENTATION AND RECONSTRUCTION WITH CAMERA POSITIONS CONSTRAINED BY GNSS MEASUREMENTS

Abstract. Photogrammetric applications using 360° images are becoming more and more popular in different fields, such as cultural heritage documentation of narrow spaces; civil, architectural, and environmental projects like tunnel surveying; mapping of urban city centres, etc. The popularity of 360° photogrammetry relates to the high productivity of the acquisition phase, giving the opportunity to capture the entire scene around the user in a relatively short time. On the other hand, the photogrammetric workflow needs ground control points (GCPs), well distributed over the survey area, to georeference the produced 3D data. Placing, measuring on-field, and identifying GCP on images is time-consuming and sometimes even not feasible due to environmental conditions. While effective solutions exist for UAV-based projects, direct georeferencing and GNSS assisted photogrammetry is still not fully exploited for ground-based acquisitions. This paper aims at presenting a solution coupling 360° images and high-precision GNSS systems for direct georeferencing of outdoor projects without the need for manually measuring GCPs. Three different acquisition modes for 360° images and GNSS data are presented, and orientation results are compared with manually measured Check Points.

Cross-scale erosion on shore platforms using the micro-erosion meter and Structure-from-Motion (SfM) photogrammetry

Investigations comparing surface downwearing rates derived from photogrammetry data and the micro-erosion meters (MEM) are limited due to the former's coarse spatial resolution and the temporal scale of data collection. These space and time limitations were revisited with the possibility of simultaneously using the Structure-from-Motion (SfM) photogrammetric workflow and the MEM. Using a cross-scale nested survey approach over one year to cover micro (10−2 m2) and meso (100 m2)-spatial scales, this paper presents erosion rates on uplifted intertidal shore platforms and marine terraces at Kaikōura Peninsula, New Zealand. The survey represents one of the first investigations combining the MEM and the SfM technique to estimate erosion rates on horizontal coastal bedrock. In a unique way, we applied established methods which allowed comparative accuracy between models via similar point matching to provide a detailed representation and visualisation of the changing rock surfaces at similar spatial and temporal scales. Over one year, across a microscale area (~0.01 m2) of rock, the MEM and SfM point clouds (SfM PCs) measured the lowest and highest erosion rates on a similar rock type and erosion site. The mean annual erosion rate of 2.244 mm/yr from the MEM was lower than the rates of 2.608 mm/yr and 10.299 mm/yr estimated using the SfM micro- and mesoscale PCs, respectively. A lower range of erosion rates (0.131 to 4.750 mm/yr) was measured using the MEM compared to erosion rates estimated from the SfM microscale PCs (0.194 to 10.106 mm/yr). Across the mesoscale (1 m2) areas, higher erosion rates were recorded using the SfM mesoscale PCs on all but one erosion site showing that a significant amount of erosion was undetected by the MEM. Using the SfM-orthomosaics, we link the erosion rates measured across the micro and mesoscale areas to intense granular disintegration, flaking, micro- and polygonal cracking and biological activities. These results provide statistical evidence to argue against extrapolating MEM erosion data obtained over small areas to larger areas on shore platforms affected by recent tectonic uplift. The implications of these findings are considered within the context of techniques for measuring shore platform erosion across spatial and temporal scales.

Quick bathymetry mapping of a Roman archaeological site using RTK UAS-based photogrammetry

Recent technological advances are leading numerous researchers and professional users to the adoption of photogrammetric products for a wide range of geoscientific applications. Especially, drone-based Structure-from-Motion (SfM) photogrammetry is often applied as a high-resolution topographic modelling technique with advantages in terms of time and cost when compared to traditional surveying approaches. The aim of this work, carried out within the GeoArchaeo Sites Project, is to investigate the adaptability of drone-based surveys, even with a targetless approach, and to estimate bathymetrical accuracies in shallow waters. The approach was applied on an underwater site to show the potential for the digitalization and monitoring of an archaeological coastal geosystem in central Italy. Specifically, this work has compared the photogrammetric surveying capability of two drones including a Phantom 4 RTK (‘P4RTK’) and a low-cost Mavic Mini 2 (‘MM2’) and an Olympus TG-6 (underwater camera) for a site submerged with a maximum depth of ∼1.6 m. The assessment of the drone SfM-based products was performed through area-based and point-wise comparisons. Specifically, the area-based were assessed through an underwater photogrammetric survey obtained by acquiring images by an operator snorkeling along a portion of the site of interest. The point-wise comparison was performed using data acquired with a Global Navigation Satellite System (GNSS). This study demonstrates that coupling SfM-photogrammetry and UAS-based surveys have potential to define submerged topography. In particular, the imagery acquired with the P4RTK survey can produce dense 3D models of the underwater surface with high resolution (about 0.02 m) and bathymetric measurements with a vertical accuracy ranging between 0.06 and 0.29 m for the area-based and point-wise analysis, respectively. Thus, the approach adopted and tested involving the use of a P4RTK has the potential to reduce constraints and limitations in terms of GCPs distribution and measurement. Also, with such an approach the need for qualified operators for underwater photogrammetric workflow can be avoided.

La documentazione del patrimonio culturale perduto mediante fotogrammetria e intelligenza artifi ciale

This research aims to give an analysis and assessment through the extraction of metricinformation from historical images and to experiment with its potentialities in the heritage fi eldwith the aim of valorising historical iconographical documentation. In particular the paperdeals with photogrammetry applied to historical images. A classifi cation and a state of the artof historical archives material considering their possible use in metric documentation and thussuitable for photogrammetry is performed. A photogrammetric workfl ow is proposed to processhistorical images and the maximum metric quality level reachable by the photogrammetricprocessing is investigated. Two case studies in Paris were chosen: the UNESCO HeritageTour Saint Jacques and the pavilions of Les Halles of the architect Victor Baltard. The twostudies represent different situations of heritage because the tower was transformed over timebut still exists and the pavilions were destroyed in 1971. Thus, it is possible to compare thedifferent results obtained from the implementation of the workfl ow to the two case studies.

Evaluating the Performance of Airborne and Ground Sensors for Applications in Precision Agriculture: Enhancing the Postprocessing State-of-the-Art Algorithm.

The main goals of the following paper are to evaluate the performance of two multispectral airborne sensors and compare their image data with in situ spectral measurements. Moreover, the authors aim to present an enhanced workflow for processing multitemporal image data using both commercial and open-source solutions. The research was provoked by the need for a relevant comparison between airborne and ground sensors for vegetation analysis and monitoring. The research team used an eBee fixed-wing platform and the multiSPEC 4c and Sequoia sensors. The authors carried out field measurements using a handheld spectrometer by Trimble—GreenSeeker. There were two flight campaigns which took place near the village of Tuhan in the Czech Republic. The results from the first campaign were discouraging, showing less possibility in the correlation between the aerial and field data. The second campaign resulted in a very high percentage of correlation between both types of data. The researchers present the image processing steps and their enhanced photogrammetric workflow for multitemporal data which helps experts and nonprofessionals to reduce their processing time.

Spatial Modelling of Vineyard Erosion in the Neszmély Wine Region, Hungary Using Proximal Sensing

Intense soil erosion in the northern part of the Gerecse Hills, Hungary, is causing significant damage to vineyards in the area. Three vineyards in the Neszmély Wine Region were investigated to quantify the amount of eroded soils. The method was based on monitoring vineyards for one-year between July 2019 and June 2020. Every season, a set of photographs of the vineyards were taken from an unmanned aerial vehicle. The images were processed in a photogrammetric workflow to produce high-resolution digital terrain models (DTMs) and orthophotos, which were used to estimate the soil loss using the Universal Soil Loss Equation (USLE) model. Particular attention was paid to the effect of seasonal variation in vegetation cover and rainfall, and the erosion control effect of the inter-row grassing already applied in the vineyards was also modelled. The results confirm and quantify the extent to which intense summer rainfall has a more significant effect on erosion compared to autumn or winter rainfall.

UAVs/drones for photogrammetry and remote sensing: Nineveh archaeological region as a case study

Unmanned Aerial Vehicle (UAV) is nowadays a valuable source of data for inspection, surveillance, mapping and 3D modeling issues. UAVs can be considered as a low cost alternative to the classical manned aerial photogrammetry, because they provide users with high spatial resolution images up to a centimeter limit. Following a typical photogrammetric workflow, 3D results like Digital Surface or Terrain Models (DTM/DSM), contours, textured 3D models, vector information, etc. can be produced, even on large areas. The paper reports the state of the art of UAV for Geomatics applications, giving an overview of different UAV platforms, applications and showing also the latest developments of UAV programs. The research presents the sequence of operations should be follow on the images taken with these aircraft for maximum use in remote sensing and air survey. Recent photographs were taken using the DJI Phantom 4 UAV (which was awarded to the Nineveh Department of Antiquities and Heritage by the Italian Mission) for the archaeological Nineveh region in Mosul city and use as a case study.

Generation of hyperspectral point clouds: Mapping, compression and rendering

Hyperspectral data are being increasingly used for the characterization and understanding of real-world scenarios. In this field, UAV-based sensors bring the opportunity to collect multiple samples from different viewpoints. Thus, light-material interactions of real objects may be observed in outdoor scenarios with a significant spatial resolution (5 cm/pixel). Nevertheless, the generation of hyperspectral 3D data still poses challenges in post-processing due to the high geometric deformation of images. Most of the current solutions use both LiDAR (Light Detection and Ranging) and hyperspectral sensors, which are integrated into the same acquisition system. However, these present several limitations due to errors derived from inertial measurements for data fusion and the spatial resolution according to the LiDAR capabilities. In this work, a method is proposed for the generation of hyperspectral point clouds. Input data are formed by push-broom hyperspectral images and 3D point clouds. On the one hand, the point clouds may be obtained by applying a typical photogrammetric workflow or LiDAR technology. Then, hyperspectral images are geometrically corrected and aligned with the RGB orthomosaic. Accordingly, hyperspectral data are ready to be mapped on the 3D point cloud. This procedure is implemented on the GPU by testing which points are visible for each pixel of the hyperspectral imagery. This work also provides a novel solution to generate, compress and render 3D hyperspectral point clouds, enabling the study of geometry and the hyperspectral response of natural and artificial materials in the real world.

ACCURACY ANALYSIS OF ESTIMATED CAMERA ORIENTATION PARAMETERS OF HISTORICAL IMAGES – CASE STUDY OF GEORG-SCHUMANN-BAU IN DRESDEN (GERMANY)

Abstract. Digitalization in archives provides access to an ever-increasing amount of historical photographs. As most of these are not taken by photogrammetric experts it is of interest if these images can be integrated into photogrammetric workflows in order to use them for advanced visualizations. While the pose estimation of historical images can be solved using feature matches retrieved by neural networks it is still unclear how accurate the determined poses are. This contribution tries to provide a reference for unordered historical photo collections with additional contemporary images using the example of the Georg-Schumann-Bau in Dresden, Germany. Therefor, a calibrated camera is used to take images of the building with the idea to enhance the estimated camera parameters of historical images in a combined model. This reference can then be used for comparison with a model created using exclusively historical images. Only half of the estimated poses are below 10 meters compared to the reference which falls behind expectations. Nonetheless, it is possible to use the estimated poses for evaluating aggregations of historical images.

COMPARING ACCURACY OF ULTRA-DENSE LASER SCANNER AND PHOTOGRAMMETRY POINT CLOUDS

Abstract. Massive point clouds have now become a common product from surveys using passive (photogrammetry) or active (laser scanning) technologies. A common question is what is the difference in terms of accuracy and precision of different technologies and processing options. In this work four ultra-dense point-clouds (PCs) from drone surveys are compared. Two PCs were created from imagery using a photogrammetric workflow, with and without ground control points. The laser scanning PCs were created with two drone flights with Riegl MiniVUX-3 lidar sensor, resulting in a point cloud with ~300 million points, and Riegl VUX-120 lidar sensor, leading to a point cloud with ~1 billion points. Relative differences between pairs from permutations of the four PCs are analysed calculating point-to-point distances over nearest neighbours. Eleven clipped PC subsets are used for this task. Ground control points (GCPs) are also used to assess residuals in the two photogrammetric point clouds in order to quantify the improvement from using GCPs vs not using GCPs when processing the images.Results related to comparing the two photogrammetric point clouds with and without GCPs show an improvement of average absolute position error from 0.12 m to 0.05 m and RMSE from 0.03 m to 0.01 m. Point-to-point distances over the PC pairs show that the closest point clouds are the two lidar clouds, with mean absolute distance (MAD), median absolute distance (MdAD) and standard deviation of distances (RMSE) respectively of 0.031 m, 0.025 m, 0.019 m; largest difference is between photogrammetric PC with GCPs, with 0.208 m, 0.206 m and 0.116 m, with the Z component providing most of the difference. Photogrammetry without GCP was more consistent with the lidar point clouds, with MAD of 0.064 m, MdAD of 0.048 m and RMSE value of 0.114 m.

Research and Development of a Photogrammetric Condition Monitoring Workflow to Improve Change Detection in Wall Paintings

ABSTRACT In the field of wall painting conservation, condition monitoring plays a key role in assessing the stability of a painting by determining if detrimental change is occurring over time. Condition monitoring typically involves a visual comparison between the painting and a previously captured photograph or image and relies on the ability of a conservator to observe visible change. In practice, the monitoring of wall paintings can be challenging given the potential range of condition phenomena, complexity of material composition, uneven surface topography and issues of scale, access, and lighting. Using two-dimensional images alone to detect change, across large surface areas and in depth, is often insufficient and can result in misleading or incomplete assessments of condition. To improve the detection of change in wall paintings, the Getty Conservation Institute initiated research between conservators and heritage recording specialists to update and validate a photogrammetric condition monitoring workflow that can identify three-dimensional change using readily available and cost-effective equipment. This paper describes the workflow and the experimental trials conducted to characterize its precision and accuracy. The results of this initial research demonstrate that an affordable, photogrammetric workflow can reliably detect and quantify sub-millimeter change in wall paintings, improving current condition monitoring practice.