Search Results (61)

Research regarding airborne laser scanning (ALS) point cloud semantic segmentation typically revolves around supervised machine learning, which requires time-consuming generation of training data. Therefore, the models are usually trained using one of the benchmarking datasets that cover a small area. Recently, many European countries published classified ALS data, which can be potentially used for training models. However, a review of the classification schemes of these datasets revealed that these schemes vary substantially, therefore limiting their applicability. Thus, our goal was three-fold. First, to develop a common classification scheme that can be applied for the semantic segmentation of various ALS datasets. Second, to unify the classification scheme of existing ALS datasets. Third, to employ them for the training of a classifier that will be able to classify data from different sources and will not require additional training. We propose a classification scheme of four classes: ground and water, vegetation, buildings and bridges, and ‘other’. The developed classifier is trained jointly using ALS data from Austria, Switzerland, and Poland. A test on unseen datasets demonstrates that the achieved intersection over union accuracy varies between 90.0–97.3% for ground and water, 68.0–95.9% for vegetation, 77.6–94.8% for buildings and bridges, and 13.5–52.7% for ‘other’. As a result, we conclude that the developed method generalizes well to previously unseen data. Full article

Background: Emergency department (ED) triage systems aim to prioritize patients based on clinical severity, ensuring timely intervention for high-risk cases. Recently, the National Early Warning Score (NEWS) has been proposed as an alternative to traditional triage systems, but its efficacy across multiple clinical outcomes remains unclear. This study aimed to compare the predictive performance of the NEWS and the Manchester Triage System (MTS) across multiple clinical outcomes. Methods: We conducted a retrospective, single-center study at Merano Hospital, Italy, from 1 June 2022 to 30 June 2023, comparing the performance of the NEWS and the Manchester Triage System (MTS). All adult ED patients (≥18 years) were included, while exclusions applied to those on fast-track pathways, non-residents, and pregnant patients. Primary outcomes included 30-day mortality, hospitalization, and ICU admission. A random 5% subgroup was analyzed for secondary outcomes, including the need for life-saving interventions (LSIs), physician-defined clinical priority, and severity. Predictive performance was assessed using Receiver Operating Characteristic (ROC) curves, area under the ROC curve (AUROC) comparisons, and Decision Curve Analysis (DCA). Results: Among 27,238 patients, the NEWS predicted 30-day mortality more accurately than the MTS (AUROC 0.745 vs. 0.701, p < 0.001). However, the MTS outperformed the NEWS for hospitalization (AUROC 0.733 vs. 0.609, p < 0.001), ICU admission (AUROC 0.862 vs. 0.672, p < 0.001), and all secondary outcomes. DCA further confirmed MTS’s superiority across clinically relevant ED probability thresholds (20–40%). Conclusions: The NEWS, while effective for predicting mortality, it is inadequate in comprehensive triage decision-making. The MTS remains the superior system for prioritizing high-risk patients based on clinical severity. Rather than replacing triage with the NEWS, efforts should focus on refining existing systems to improve risk stratification. Future multi-center prospective studies are necessary to validate these findings. Full article

Background: This study aimed to evaluate Emergency Department and Intensive Care Unit nurses’ skills in interpreting blood gas analysis results and to use those interpretations in clinical decision-making. Methods: In this prospective, multicenter, simulation-based study, nurses from the Emergency Department (ED) of Merano Hospital and the Intensive Care Unit (ICU) of Bolzano Hospital, Italy, were presented with 16 clinical vignettes based on real patient cases. These vignettes were designed to evaluate the nurses’ ability to identify patients with time-dependent conditions and recommend appropriate therapeutic interventions. Outcomes measured included sensitivity, specificity, and agreement with physician-assigned urgency levels and therapy recommendations. Results: Among the 43 participants (26 ICU and 17 ED nurses), specificity in excluding patients without time-dependent conditions or organ replacement needs was high. However, sensitivity in identifying time-dependent conditions was less than 50%. Agreement with physician-assigned urgency levels was low, with Cohen’s kappa values of 0.139 for ICU nurses and 0.218 for ED nurses. Nurses with lower self-confidence in interpreting BGA results made more errors, while other personal or professional factors did not significantly impact performance. Conclusions: Although critical care nurses can effectively rule out patients without time-dependent conditions, their ability to identify such conditions requires improvement. These findings underscore the need for targeted training programs to enhance nurses’ BGA interpretation skills and clinical decision-making in high-pressure, time-sensitive situations. Full article

The present study focuses on the preservation of historic timber constructions, crucial cultural heritage assets that demand effective structural health monitoring (SHM) to ensure safety and integrity. SHM aims to detect and evaluate potential structural deviations that may compromise performance, requiring both detailed geometric data acquisition and 3D modeling. For this purpose, contactless tools such as photogrammetry, laser scanning, and other topographic methods are employed to gather point cloud data. This research utilizes a terrestrial laser scanner (TLS) to generate 3D models of the historic timber tower of St. Michaeler church in Vienna. A novel automated modeling method is compared with two manual modeling approaches. The first is a traditional as-designed structural model created in Dlubal RSTAB software, and the second is a manually generated as-built model created using a scan-to-BIM application in Revit. While the first model is based on 2D plan documents created from the TLS point cloud, the second and automated models use the point cloud as direct input. The findings demonstrate that this automated model significantly enhances early-stage structural assessment efficiency, providing reliable insights into structural conditions with minimal processing time. This research underscores the potential of automated 3D modeling in preliminary structural assessments of historic timber structures. Full article

With LiDAR (Light Detection and Ranging) time series being used for various applications, the optimal realization of a common geodetic datum over many epochs is a highly important prerequisite with a direct impact on the accuracy and reliability of derived measures. In our work, we develop and define several approaches to the adjustment of multi-temporal LiDAR data in a given software framework. These approaches, ranging from pragmatic to more rigorous solutions, are applied to an 8-year time series with 21 individual epochs. The analysis of the respective results suggests that a sequence of bi-temporal adjustments of each individual epoch and a designated reference epoch brings the best results while being more flexible and computationally viable than the most extensive approach of using all epochs in one single multi-temporal adjustment. With a combination of sparse control patches measured in the field and one selected reference block, the negative impacts of changing surfaces on orientation quality are more effectively avoided than in any other approach. We obtain relative discrepancies in the range of 1–2 cm between epoch-wise DSMs for the complete time series and mean offsets from independent checkpoints in the range of 3–5 cm. Based on our findings, we formulate design criteria for setting up and adjusting future time series with the proposed method. Full article

Terrestrial LiDAR scanning (TLS) has the potential to revolutionize forestry by enabling the precise estimation of aboveground biomass, vital for forest carbon management. This study addresses the lack of comprehensive benchmarking for leaf-filtering algorithms used in TLS data processing and evaluates four widely recognized geometry-based leaf-filtering algorithms (LeWoS, TLSeparation, CANUPO, and a novel random forest model) across openly accessible TLS datasets from diverse global locations. Multiple evaluation dimensions are considered, including pointwise classification accuracy, volume comparisons using a quantitative structure model applied to wood points, computational efficiency, and visual validation. The random forest model outperformed the other algorithms in pointwise classification accuracy (overall accuracy = 0.95 ± 0.04), volume comparison (R-squared = 0.96, slope value of 0.98 compared to destructive volume), and resilience to reduced point cloud density. In contrast, TLSeparation exhibits the lowest pointwise classification accuracy (overall accuracy = 0.81 ± 0.10), while LeWoS struggles with volume comparisons (mean absolute percentage deviation ranging from 32.14 ± 29.45% to 49.14 ± 25.06%) and point cloud density variations. All algorithms show decreased performance as data density decreases. LeWoS is the fastest in terms of processing time. This study provides valuable insights for researchers to choose appropriate leaf-filtering algorithms based on their research objectives and forest conditions. It also hints at future possibilities for improved algorithm design, potentially combining radiometry and geometry to enhance forest parameter estimation accuracy. Full article

Nonrigid registration presents a significant challenge in the domain of point cloud processing. The general objective is to model complex nonrigid deformations between two or more overlapping point clouds. Applications are diverse and span multiple research fields, including registration of topographic data, scene flow estimation, and dynamic shape reconstruction. To provide context, the first part of the paper gives a general introduction to the topic of point cloud registration, including a categorization of existing methods. Then, a general mathematical formulation for the point cloud registration problem is introduced, which is then extended to address also nonrigid registration methods. A detailed discussion and categorization of existing approaches to nonrigid registration follows. In the second part of the paper, we propose a new method that uses piecewise tricubic polynomials for modeling nonrigid deformations. Our method offers several advantages over existing methods. These advantages include easy control of flexibility through a small number of intuitive tuning parameters, a closed-form optimization solution, and an efficient transformation of huge point clouds. We demonstrate our method through multiple examples that cover a broad range of applications, with a focus on remote sensing applications—namely, the registration of airborne laser scanning (ALS), mobile laser scanning (MLS), and terrestrial laser scanning (TLS) point clouds. The implementation of our algorithms is open source and can be found our public repository. Full article

Background: Infections in emergency departments (EDs) are insidious clinical conditions characterised by high rates of hospitalisation and mortality in the short-to-medium term. The serum albumin, recently demonstrated as a prognostic biomarker in septic patients in intensive care units, could be an early marker of severity upon arrival of infected patients in the ED. Aim: To confirm the possible prognostic role of the albumin concentration recorded upon arrival of patients with infection. Methods: A prospective single-centre study was performed in the ED of the General Hospital of Merano, Italy, between 1 January 2021 and 31 December 2021. All enrolled patients with infection were tested for serum albumin concentration. The primary outcome measure was 30-day mortality. The predictive role of albumin was assessed by logistic regression and decision tree analysis adjusted for Charlson comorbidity index, national early warning score, and sequential organ failure assessment (SOFA) score. Results: 962 patients with confirmed infection were enrolled. The median SOFA score was 1 (0–3) and the mean serum albumin level was 3.7 g/dL (SD 0.6). Moreover, 8.9% (86/962) of patients died within 30 days. Albumin was an independent risk factor for 30-day mortality with an adjusted hazard ratio of 3.767 (95% CI 2.192–6.437), p < 0.001. Decision tree analysis indicated that at low SOFA scores, albumin had a good predictive ability, indicating a progressive mortality risk reduction in concentrations above 2.75 g/dL (5.2%) and 3.52 g/dL (2%). Conclusions: Serum albumin levels at ED admission are predictive of 30-day mortality in infected patients, showing better predictive abilities in patients with low-to-medium SOFA scores. Full article

Aerial photographs of the European Alps usually only reach back to the middle of the 20th century, which limits the time span of corresponding studies that quantitatively analyse long-term surface changes of proglacial areas using georeferenced orthophotos. To the end of the Little Ice Age, this leads to a gap of about 100 years. Using digital monoplotting and several historical terrestrial photographs, we show the quantification of surface changes of a Little Ice Age lateral moraine section until the late second half of the 19th century, reaching a total study period of 130 years (1890–2020). The (initial) gully system expands (almost) continuously over the entire study period from 1890 to 2020. Until 1953, the vegetation-covered areas also expanded (mainly scree communities, alpine grasslands and dwarf shrub communities), before decreasing again, especially between 1990 and 2003, due to large-scale erosion within the gully system. Furthermore, our results show that the land-cover development was impacted by temperature and precipitation changes. With the 130-year study period, we contribute to a substantial improvement in the understanding of the processes in the proglacial by analysing the early phase and thus the immediate response of the lateral moraine to the ice exposure. Full article

In recent decades, in the Pre-Carpathian region of Ukraine during the summer period, floods and flood events became more frequent. They were accompanied by significant economic and environmental loss. Especially powerful were the floods of 2008 and 2020, but the floods in 2014 and 2016 also had destructive consequences. Therefore, the study of river channel processes, river stability and assessment of flooded land areas due to floods is an urgent problem. The aim of the study is to propose a methodology for hydrological modeling of sections of riverbeds with complex morphometric and hydrological characteristics. The construction of a digital elevation model (DEM) and the selection of the distance between the cross-sections, as well as the determination of the Manning coefficients, have the greatest impact on the accuracy of the modeling, so these factors should be given maximum weight when calibrating the model. The object of the study was the section of the Dniester River in Ukraine in the place of transition from the foothill part of the channel to the hilly–marshy part with complex meandering and significant shifts of the river. The methodology of hydrological modeling includes three principal components: construction of the DEM, determination of the type of underlying surface and determination of the level of water rise in the riverbed. The research was carried out on the basis of imaging from unmanned aerial vehicles (UAVs). In 2017, the imaging of a section of the Dniester riverbed was carried out in the summer during a period of significant vegetation growth, which affected the accuracy of determining the heights of the model points. According to the results of this imaging, the residual mean square (RMS) for determining the heights of the points exceeded the permissible value of the RMS (0.25–0.3 m) by two times. In 2021, imaging was performed in the autumn period when there was no leaf cover. The RMS of the DEM for 2021 imaging was 0.26 m. According to the results of the survey in 2017 and 2021, orthophotoplans were created, which were used to determine the planned displacements of the river bed and clarify the Manning coefficients, which characterize the roughness of the underlying surface. The value of the water level rise was obtained on the basis of the graph on the date of the maximum rise of the water level on 24 June 2020 according to the hydrometeorological station located near the selected area. The result of the research is hydrological modeling using the HEC-RAS module for a site with complex hydrological and morphometric characteristics on the date of the maximum water rise. It was established that in order to achieve the required accuracy of the DEM, imaging should be carried out in the leafless period of the year, since the accuracy of constructing the DEM decreases by half during the growing season. On the basis of the obtained orthophoto plans, a methodology for determining refined Manning coefficients was developed, which allows taking into account changes in the underlying surface of the channel area. The area of the flooded area was calculated based on the level of water rise during the 2020 flood. Full article

Three-dimensional city models play an important role for a large number of applications in urban environments, and thus it is of high interest to create them automatically, accurately and in a cost-effective manner. This paper presents a new methodology for point cloud accuracy improvement to generate terrain topographic models and 3D building modeling with the Open Geospatial Consortium (OGC) CityGML standard, level of detail 1 (LOD1), using very high-resolution (VHR) satellite images. In that context, a number of steps are given attention (which are often (in the literature) not considered in detail), including the local geoid and the role of the digital terrain model (DTM) in the dense image matching process. The quality of the resulting models is analyzed thoroughly. For this objective, two stereo Pléiades 1 satellite images over Iasi city were acquired in September 2016, and 142 points were measured in situ by global navigation satellite system real-time kinematic positioning (GNSS-RTK) technology. First, the quasigeoid surface resulting from EGG2008 regional gravimetric model was corrected based on data from GNSS and leveling measurements using a four-parameter transformation, and the ellipsoidal heights of the 142 GNSS-RTK points were corrected based on the local quasigeoid surface. The DTM of the study area was created based on low-resolution airborne laser scanner (LR ALS) point clouds that have been filtered using the robust filter algorithm and a mask for buildings, and the ellipsoidal heights were also corrected with the local quasigeoid surface, resulting in a standard deviation of 37.3 cm for 50 levelling points and 28.1 cm for the 142 GNSS-RTK points. For the point cloud generation, two scenarios were considered: (1) no DTM and ground control points (GCPs) with uncorrected ellipsoidal heights resulting in an RMS difference (Z) for the 64 GCPs and 78 ChPs of 69.8 cm and (2) with LR ALS-DTM and GCPs with corrected ellipsoidal height values resulting in an RMS difference (Z) of 60.9 cm. The LOD1 models of 1550 buildings from the Iasi city center were created based on Pléiades-DSM point clouds (corrected and not corrected) and existing building sub-footprints, with four methods for the derivation of the building roof elevations, resulting in a standard deviation of 1.6 m against high-resolution (HR) ALS point cloud in the case of the best scenario. The proposed method for height extraction and reconstruction of the city structure performed the best compared with other studies on multiple satellite stereo imagery. Full article

We present a compact polarimeter for ${}^3$He ions with special emphasis on the analysis of short-pulsed beams accelerated during laser–plasma interactions. We discuss the specific boundary conditions for the polarimeter, such as the properties of laser-driven ion beams, the selection of the polarization-sensitive reaction in the polarimeter, the representation of the analyzing-power contour map, the choice of the detector material used for particle identification, as well as the production procedure of the required deuterated foil-targets. The assembled polarimeter has been tested using a tandem accelerator delivering unpolarized ${}^3$He ion beams, demonstrating good performance in the few-MeV range. The statistical accuracy and the deduced figure-of-merit of the polarimetry are discussed, including the count-rate requirement and the lower limit of accuracy for beam-polarization measurements at a laser-based ion source. Full article

Admired and despised, created and destroyed, legal and illegal: Contemporary graffiti are polarising, and not everybody agrees to label them as cultural heritage. However, if one is among the steadily increasing number of heritage professionals and academics that value these short-lived creations, their digital documentation can be considered a part of our legacy to future generations. To document the geometric and spectral properties of a graffito, digital photographs seem to be appropriate. This also holds true when documenting an entire graffiti-scape consisting of 1000s of individual creations. However, proper photo-based digital documentation of such an entire scene comes with logistical and technical challenges, certainly if the documentation is considered the basis for further analysis of the heritage assets. One main technical challenge relates to the photographs themselves. Conventional photographs suffer from multiple image distortions and usually lack a uniform scale, which hinders the derivation of dimensions and proportions. In addition, a single graffito photograph often does not reflect the meaning and setting intended by the graffitist, as the creation is frequently shown as an isolated entity without its surrounding environment. In other words, single photographs lack the spatio-temporal context, which is often of major importance in cultural heritage studies. Here, we present AUTOGRAF, an automated and freely-available orthorectification tool which converts conventional graffiti photos into high-resolution, distortion-free, and georeferenced graffiti orthophotomaps, a metric yet visual product. AUTOGRAF was developed in the framework of INDIGO, a graffiti-centred research project. Not only do these georeferenced photos support proper analysis, but they also set the basis for placing the graffiti in their native, albeit virtual, 3D environment. An experiment showed that 95 out of 100 tested graffiti photo sets were successfully orthorectified, highlighting the proposed methodology’s potential to improve and automate one part of contemporary graffiti’s digital preservation. Full article

Recent advances in 3D laser scanner technology have provided a large amount of accurate geo-information as point clouds. The methods of machine vision and photogrammetry are used in various applications such as medicine, environmental studies, and cultural heritage. Aerial laser scanners (ALS), terrestrial laser scanners (TLS), mobile mapping laser scanners (MLS), and photogrammetric cameras via image matching are the most important tools for producing point clouds. In most applications, the process of point cloud registration is considered to be a fundamental issue. Due to the high volume of initial point cloud data, 3D keypoint detection has been introduced as an important step in the registration of point clouds. In this step, the initial volume of point clouds is converted into a set of candidate points with high information content. Many methods for 3D keypoint detection have been proposed in machine vision, and most of them were based on thresholding the saliency of points, but less attention had been paid to the spatial distribution and number of extracted points. This poses a challenge in the registration process when dealing with point clouds with a homogeneous structure. As keypoints are selected in areas of structural complexity, it leads to an unbalanced distribution of keypoints and a lower registration quality. This research presents an automated approach for 3D keypoint detection to control the quality, spatial distribution, and the number of keypoints. The proposed method generates a quality criterion by combining 3D local shape features, 3D local self-similarity, and the histogram of normal orientation and provides a competency index. In addition, the Octree structure is applied to control the spatial distribution of the detected 3D keypoints. The proposed method was evaluated for the keypoint-based coarse registration of aerial laser scanner and terrestrial laser scanner data, having both cluttered and homogeneous regions. The obtained results demonstrate the proper performance of the proposed method in the registration of these types of data, and in comparison to the standard algorithms, the registration error was diminished by up to 56%. Full article

The number of high and very high resolution (VHR) optical satellite sensors, as well as the number of medium resolution satellites is continuously growing. However, not all high-resolution optical satellite imaging cameras have a sufficient and stable calibration in time. Due to their high agility in rotation, a quick change in viewing direction can lead to satellite attitude oscillation, causing image distortions and thus affecting image geometry and geo-positioning accuracy. This paper presents an approach based on re-projection of regularly distributed 3D ground points from object in image space, to detect and estimate the periodic distortions of Pléiades tri-stereo imagery caused by satellite attitude oscillations. For this, a hilly region was selected as a test site. Consequently, we describe a complete processing pipeline for computing the systematic height errors (deformations, waves) of the satellite-based digital elevation model by using a Lidar high resolution terrain model. Ground points with fixed positions, but with two elevations (actual and corrected) are then re-projected to the satellite images with the aid of the Rational Polynomial Coefficients (RPCs) provided with the imagery. Therefore, image corrections (displacements) are determined by computing the differences between the distinct positions of corresponding points in image space. Our experimental results in Allentsteig (Lower Austria) show that the systematic height errors of satellite-based elevation models cannot be compensated with an usual or even high number of Ground Control Points (GCPs) for RPC bias correction, due to insufficiently known image orientations. In comparison to a reference Lidar Digital Terrain Model (DTM), the computed elevation models show undulation effects with a maximum height difference of 0.88 m in along-track direction. With the proposed method, image distortions in-track direction with amplitudes of less than 0.15 pixels were detected. After applying the periodic distortion compensation to all three images, the systematic elevation discrepancies from the derived elevation models were successfully removed and the overall accuracy in open areas improved by 33% in the RMSE. Additionally, we show that a coarser resolution reference elevation model (AW3D30) is not feasible for improving the geometry of the Pléiades tri-stereo satellite imagery. Full article