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The mostly subterranean nature of district heating system pipelines makes pinpointing any occurring leakages a challenge. Airborne thermography offers a means for widespread monitoring, allowing thermal anomalies to be identified within multitudes of infrared images. This paper details a program developed to automate the entire image analysis process using mainly open-source software. Thermal images are acquired via unmanned aircraft system (UAS), pre-processed, and georeferenced individually or combined to orthomosaics. The search space is minimized to areas around the pipelines. Regions of interest are determined by image segmentation via tailored triangle histogram thresholding. The majority of resulting false alarms are removed by comparison with characteristic traits and results classified by their severity. The algorithm is applied to images newly acquired in Germany as part of a case study. The implemented methodology allows for a reduction of between 92 and 99% of thermal anomalies to a manageable amount of potential leakages for network operators to view. The use of orthomosaiking software in this context, though helpful in coalescing data, is found to lack robustness, precision and therefore reliability. Despite some limitations, the developed program is able to confidently detect and categorize leakages of varying severity and can be used directly by network operators. Future research will focus on further data pre-processing to eliminate thermal drift and remove the remaining false alarms, which mostly pertain to common urban features. [ABSTRACT FROM AUTHOR]

Visibility observations and accurate forecasts are essential in meteorology, requiring a dense network of observation stations. This paper investigates image processing techniques for object detection and visibility determination using static cameras. It proposes a comprehensive method that includes image preprocessing, landmark identification, and visibility estimation, mirroring the observation process of professional meteorological observers. This study validates the visibility observation procedure using the k-nearest neighbors machine learning method across six locations, including four in the Czech Republic, one in the USA, and one in Germany. By comparing our results with professional observations, the paper demonstrates the suitability of the proposed method for operational application, particularly in foggy and low visibility conditions. This versatile method holds potential for adoption by meteorological services worldwide. [ABSTRACT FROM AUTHOR]

A limited number of gauging stations, especially for nested catchments, hampers a process understanding of the interaction between streamflow, groundwater and water usage during drought. Non‐commercial measurement devices can help overcome this lack of monitoring, but they need to be thoroughly tested. The Dreisam River in the South‐West of Germany was affected by several hydrological drought events from 2015 to 2020 during which parts of the main stream and tributaries fell dry. Therefore it provided a useful case study area for a flexible longitudinal water quality and quantity monitoring network. Among other measurements the setup employs an image‐based method with QR codes as fiducial marker. In order to assess under which conditions the QR‐code based water level loggers (WLL) deliver data according to scientific standards, we compared its performance to conventional capacitive based WLL. The results from 20 monitoring stations reveal that the riverbed was dry for >50% at several locations and even for >70% at most severely affected locations during July and August 2020, with the north western parts of the catchment being especially concerned. Highly variable longitudinal drying patterns of the stream reaches emerged from the monitoring. The image‐based method was found valuable for identification and validation of zero level occurrences. Nevertheless, a simple image processing approach (based on an automatic thresholding algorithm) did not compensate for errors due to natural conditions and technical setup. Our findings highlight that the complexity of measurement environments is a major challenge when working with image‐based methods. [ABSTRACT FROM AUTHOR]

Although several papers have shown that bike ridership is affected by actual weather conditions, this is the first study to comprehensively investigate the impact of forecasted weather conditions on bike ridership. The results show that both actual and forecasted weather conditions can be used as useful explanatory variables for predicting bicycle usage. Even incorrect weather forecasts can impact on bike ridership, which underlines the importance of weather forecast effects for traffic planners; for example, forecasted rain can reduce bike traffic by 3.6% in periods that turn out to be rain-free. Additionally, a digital image-processing method is used to calculate the darkness of the cloud coverage displayed on weather forecast maps. The results imply that bike ridership is significantly smaller in regions with darker forecasted clouds. It is also shown that weather forecasts have a stronger impact on recreational bike traffic than on utilitarian traffic. Furthermore, various lagging and leading effects of rain forecasts are outlined. Morning rain forecasts can, for example, reduce bike ridership in midday and afternoon hours that were predicted to be rain-free. To derive these results, hourly bicycle counts from 188 automated counting stations in Germany are collected for the years 2017 and 2018. They are linked to actual weather data from Germany's National Meteorological Service and with historical weather forecasts that are deduced from weather maps of Germany's most-watched television news program. Log-linear and negative binomial regression models are then used to estimate the weather forecast effects. [ABSTRACT FROM AUTHOR]

Conventional ultrasound (US) is the first-line imaging method for abdominal pathologies, but its diagnostic accuracy is operator-dependent, and data storage is usually limited to two-dimensional images. A novel tomographic US system (Curefab CS, Munich, Germany) processes imaging data combined with three-dimensional spatial information using a magnetic field tracking. This enables standardized image presentation in axial planes and a review of the entire examination. The applicability and diagnostic performance of this tomographic US approach was analyzed in an abdominal setting using conventional US as reference. Tomographic US data were successfully compiled in all subjects of a training cohort (20 healthy volunteers) and in 50 patients with abdominal lesions. Image quality (35% and 79% for training and patient cohort respectively) and completeness of organ visualization (45% and 44%) were frequently impaired in tomographic US compared to conventional US. Conventional and tomographic US showed good agreement for measurement of organ sizes in the training cohort (right liver lobe and both kidneys with a median deviation of 5%). In the patient cohort, tomographic US identified 57 of 74 hepatic or renal lesions detected by conventional ultrasound (sensitivity 77%). In conclusion, this study illustrates the diagnostic potential of abdominal tomographic US, but current significant limitations of the tomographic ultrasound device demand further technical improvements before this and comparable approaches can be implemented in clinical practice. [ABSTRACT FROM AUTHOR]

• Automatic Plant disease diagnosis for early disease symptoms. • Novel image processing algorithm in combination with machine learning methods. • A testing on wheat was performed with 7 mobile devices over a period of 4 seasons. • Obtained results reveal AuC metrics higher than 0.80 for all the analyzed diseases. Fungal infection represents up to 50 % of yield losses, making it necessary to apply effective and cost efficient fungicide treatments, whose efficacy depends on infestation type, situation and time. In these cases, a correct and early identification of the specific infection is mandatory to minimize yield losses and increase the efficacy and efficiency of the treatments. Over the last years, a number of image analysis-based methodologies have been proposed for automatic image disease identification. Among these methods, the use of Deep Convolutional Neural Networks (CNNs) has proven tremendously successful for different visual classification tasks. In this work we extend previous work by Johannes et al. (2017) with an adapted Deep Residual Neural Network-based algorithm to deal with the detection of multiple plant diseases in real acquisition conditions where different adaptions for early disease detection have been proposed. This work analyses the performance of early identification of three relevant European endemic wheat diseases: Septoria (Septoria triciti), Tan Spot (Drechslera triciti-repentis) and Rust (Puccinia striiformis & Puccinia recondita). The analysis was done using different mobile devices, and more than 8178 images were captured in two pilot sites in Spain and Germany during 2014 , 2015 and 2016. Obtained results reveal an overall improvement of the balanced accuracy from 0.78 (Johannes et al., 2017) up to 0.87 under exhaustive testing, and balanced accuracies greater than 0.96 on a pilot test performed in Germany. [ABSTRACT FROM AUTHOR]

Highlights • Time-related factors and distances-related factors affect the drivers' deceleration rate choice in Vehicle-Pedestrian conflict have been analyzed. • The drivers' effective decelerating zone with sight angle has been proposed with the consideration of longitudinal & lateral conflict behavior. • Intercultural comparison has been made to address the significant differences in driver behavior in China and Germany. Abstract To analyse the drivers' decelerating behavior in conflict avoidance process under Vehicle-Pedestrian conflict situation is accordingly a surrogate method for the guidance of driver evasive manoeuvre and improving the active driving safety. Field observation data has been collected by video recording and image processing at urban unsignalized midblock crosswalks in Beijng, China and Munich, Germany. The relationship between deceleration rate and vehicle speed has been statistically described. By analyzing how the time-related measures (Vehicle Time to Conflict Point and Pedestrian Time to Conflict Point) and the distance-related measures (Vehicle longitudinal distance to Conflict Point and Pedestrian Lateral distance to Conflict Point) affect the deceleration rate choice, the drivers' effective decelerating zone with sight angle has been proposed with the consideration of longitudinal & lateral conflict behavior. Minimum Vehicle-Pedestrian conflict distance is calculated and a 2 m minimum distance is suggested as the threshold for collision discrimination. Comparisons which address the needs to make the intercultural adaptation when introducing Driver Assistance Systems to developing countries have also been made to show the significant differences between China and Germany. The whole work could hopefully be the groundwork in the target of conflict pre-scanning by decelerating zones under various conflict situations and the design of vehicle active safety systems. [ABSTRACT FROM AUTHOR]

This research captures local networks of German political parties and welfare agencies in regards to poverty. The article explores whether there are differences in regards to homophily and brokerage between the two studied groups using a dataset of 33 egonetworks in two German cities. The computer assisted drawn networks were collected in an interactive participative way together with the interviewed egonetworks. To achieve the theoretical aim of analysing homophily and brokerage between politicians and welfare workers, two hypotheses are examined, resting upon social capital theory. The hypotheses were quantified and explicated with different variables. The first hypothesis states that heterophile networks imply more social capital, which referred to different measurements (size, density, homophily). This could be partially validated since the analysed networks of association representatives (n=12) were denser and slightly more heterophile than those of party representatives (n=21). Second, it was assumed that politicians, because of their function as elected representatives, would be more likely to take on an interface function within the communities than representatives of civil society institutions. Results based on calculated EI-indices, subgraphs and brokerage show that party representatives do indeed have larger networks, but these networks split into fewer subgraphs than association representatives' networks. [ABSTRACT FROM AUTHOR]

Objective. To monitor size and shape changes of bone erosions and changes in BMD in the vicinity of the erosion and in the periarticular trabecular compartment of patients with RA using high-resolution peripheral quantitative CT (HR-pQCT) imaging and to compare an automated three-dimensional (3D) image processing technique with manual measurements of erosion width and depth. Methods. The shape of 40 bone erosions and composition of bone around the erosions were analysed in the MCP joints of 22 RA patients both manually and by semi-automated 3D image processing at two different time points. Periosteal segmentation was performed using volume growing and morphological operations. Image registration was applied for transfer of baseline segmentations to follow-up datasets. Results. Eight erosions decreased in size, 6 increased and 28 remained stable. Increasing erosions were more spherical and smaller at baseline compared with decreasing or stable erosions. BMD in the vicinity of shrinking erosions increased, while it decreased next to expanding erosions. There was moderate agreement in the determination of erosion volume between semi-automated and manual measurements, but agreement was poor when assessing changes in volume over time. Conclusion. Longitudinal changes in erosion size and shape and of BMD in the vicinity of an erosion can be measured. BMD changes are associated with progression and regression of erosions. However, the semi-automated and manual approaches did not classify longitudinal changes of erosion volume in the same way. Further research is necessary to define the nature of these differences. [ABSTRACT FROM AUTHOR]

Especially for investigator-initiated research at universities and academic institutions, Internet-based rare disease registries (RDR) are required that integrate electronic data capture (EDC) with automatic image analysis or manual image annotation. We propose a modular framework merging alpha-numerical and binary data capture. In concordance with the Office of Rare Diseases Research recommendations, a requirement analysis was performed based on several RDR databases currently hosted at Uniklinik RWTH Aachen, Germany. With respect to the study management tool that is already successfully operating at the Clinical Trial Center Aachen, the Google Web Toolkit was chosen with Hibernate and Gilead connecting a MySQL database management system. Image and signal data integration and processing is supported by Apache Commons FileUpload-Library and ImageJ-based Java code, respectively. As a proof of concept, the framework is instantiated to the German Calciphylaxis Registry. The framework is composed of five mandatory core modules: (1) Data Core, (2) EDC, (3) Access Control, (4) Audit Trail, and (5) Terminology as well as six optional modules: (6) Binary Large Object (BLOB), (7) BLOB Analysis, (8) Standard Operation Procedure, (9) Communication, (10) Pseudonymization, and (11) Biorepository. Modules 1-7 are implemented in the German Calciphylaxis Registry. The proposed RDR framework is easily instantiated and directly integrates image management and analysis. As open source software, it may assist improved data collection and analysis of rare diseases in near future. [ABSTRACT FROM AUTHOR]

A new semi-automated method is proposed in this study that detects joint traces from digital images and calculates the length distribution through three-dimensional data structuring. The method comprises detecting the pixels corresponding to the joint trace in digital images and calculating the length distribution through three-dimensional data structuring of the pixels detected as the joint trace. Semantic segmentation based on deep learning techniques is applied to the detection of joint trace pixels to overcome the limitations of rule-based image processing techniques. For training a deep learning network, various rock joint images were sampled and labeled, following which the data sets for training, validation, and testing were prepared through classification and augmentation. Using the prepared datasets, the performance of the classifiers based on widely used semantic segmentation networks, namely U-Net (University of Freiburg, Germany) network and DeepLabV3+ (Google, USA) network, was compared. The classifier based on the DeepLabV3+ network was ultimately selected and applied. The trained classifier successfully detected pixels corresponding to the joint trace in images of both flat and uneven rock surfaces, indicating that the deep learning technique could be applied effectively to joint trace detection from digital images. Further, a data structuring technique is proposed to calculate the joint trace length using pixelwise data detected through the trained classifier. This technique uses point cloud data obtained by photogrammetry, and comprises two-dimensional thinning and segmentation, three-dimensional projection, segmentation, and segment linking. The entire proposed method, from detecting the joint trace pixels to three-dimensional data structuring, was verified by applying it to both simple flat and uneven rock surface models. • New technique for joint trace detection using deep learning network is proposed. • New data structuring technique for pixelwise data is proposed. • Semi-automatic calculation of the trace length is possible using the two techniques. • Proposed techniques is verified by applying it to both flat and uneven rock surfaces. [ABSTRACT FROM AUTHOR]

Skin cancer has become the most common cancer type. Research has applied image processing and analysis tools to support and improve the diagnose process. Conventional procedures usually centralise data from various data sources to a single location and execute the analysis tasks on central servers. However, centralisation of medical data does not often comply with local data protection regulations due to its sensitive nature and the loss of sovereignty if data providers allow unlimited access to the data. The Personal Health Train (PHT) is a Distributed Analytics (DA) infrastructure bringing the algorithms to the data instead of vice versa. By following this paradigm shift, it proposes a solution for persistent privacy- related challenges. In this work, we present a feasibility study, which demonstrates the capability of the PHT to perform statistical analyses and Machine Learning on skin lesion data distributed among three Germany-wide data providers.

Traditional approaches to modeling and processing discrete pixels are mainly based on image features or model optimization. These methods often result in excessive shrinkage or expansion of the restored pixel region, inhibiting accurate recovery of the target pixel region shape. This paper proposes a simultaneous source and mask-images optimization model based on skeleton divergence that overcomes these problems. In the proposed model, first, the edge of the entire discrete pixel region is extracted through bilateral filtering. Then, edge information and Delaunay triangulation are used to optimize the entire discrete pixel region. The skeleton is optimized with the skeleton as the local optimization center and the source and mask images are simultaneously optimized through edge guidance. The technique for order of preference by similarity to ideal solution (TOPSIS) and point-cloud regularization verification are subsequently employed to provide the optimal merging strategy and reduce cumulative error. In the regularization verification stage, the model is iteratively simplified via incremental and hierarchical clustering, so that point-cloud sampling is concentrated in the high-curvature region. The results of experiments conducted using the moving-target region in the RGB-depth (RGB-D) data (Technical University of Munich, Germany) indicate that the proposed algorithm is more accurate and suitable for image processing than existing high-performance algorithms. [ABSTRACT FROM AUTHOR]

Since 1980, severe thunderstorms have caused nearly USD 3 billion in damages annually inEurope. Hailstorms account for a significant portion of thunderstorm-related losses, yet theclimatological aspects of large hail are not well understood. While the European SevereWeather Database (ESWD) contains the most comprehensive archive of hail observations inEurope, spatial and temporal inhomogeneities and discontinuities preclude the database frombeing used to create a representative long-term hail climatology. We therefore propose amethod to generate a hail event set for Central Europe by: 1) objectively identifying recenthail swaths from high-resolution radar data, and 2) combining the identified hail swathproperties with a probabilistic convective hazard model and ESWD hail observations tosimulate individual hailstorms. Such a hail event set will facilitate the evaluation of theclimatological aspects of large hail and time-dependent changes in the frequency ofhailstorms. In addition, the hail event set will be combined with exposure and vulnerabilitydata to produce detailed loss estimates and better understand the risk of large hail inEurope. Hail swaths occurring in Germany over a 4-year period (2015–2018) were identifiedusing the vertically integrated ice (VII) product from the Deutscher Wetterdienst (DWD).Specifically, we isolated continuous regions where 6-hour accumulated VII satisfied aminimum intensity (25 kg m−3) criterion, and then used Python image processing to generatehail swath ellipses and analyze their spatial characteristics. Next, for each 6-hour intervalduring the 1979–2018 period, we applied the convective hazard model to ERA-Interimreanalysis data to simulate the occurrence/non-occurrence of hail in each reanalysis grid cell.If hail was simulated in a given grid cell, we also predicted the number of hail swaths in thegrid cell during the 6-hour period. For each hail swath, we simulated the length,area, and orientation based on the spatial properties of the previously identified hailswaths. In addition, we simulated the maximum hail diameter based on the statisticaldistributions of observed hail diameter under various instability and deep-layer shearscenarios. Results from the VII hail detection algorithm suggest that large hail in Germany occursprimarily after 12 UTC, with a maximum frequency during the afternoon and early evening(12–18 UTC). While the majority of identified hail swaths affected areas less than 100km2, hail swaths exceeding 500 km2 were not especially uncommon. Hail swathsgenerally followed trajectories between 225˚ (southwest-to-northeast) and 315˚ (northwest-to-southeast) and were typically associated with right-moving thunderstorm cellswhen the synoptic-scale environment was moderately-to-strongly sheared. The simulated hailswaths exhibit a meridional gradient in the predicted frequency of large hail, with thelowest frequencies near the North and Baltic Seas, and the highest frequencies overthe eastern Alps. The number of simulated hail cases peaks in July and August,whereas large hail as reported in the ESWD is most prevalent between May and July. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the performance of an innovative image processing approach for detection of T2-weighted hyperintense multiple sclerosis (MS) lesions., Methods: In this study 20 consecutive patients with inflammatory demyelinating lesions were retrospectively evaluated of whom 10 patients featured progressive disease and 10 a stable lesion load. 3 mm transversal FLAIRfusion imaging was processed and archived. Image processing was performed through landmark-based 3D co-registration of the previous and current isotropic FLAIR examination followed by inversion of image contrast. Thereby, the hyperintense signals of the unchanged MS plaques extinguish each other, while newly developed lesions appear bright on FLAIRfusion. Diagnostic performance was evaluated by 4 experienced readers. Consensus reading supplied the reference standard. Sensitivity, specificity, NPV (negative predictive value), PPV (positive predictive value), interreader agreement and reading time were the outcome measures analyzed., Results: Combined sensitivity was 100% at a specificity of 88.2%, with PPV ranging from 83.3% to 90.1% and NPV at 100%. Reading time was nearly 5‑fold faster than conventional side by side comparison (35.6 s vs. 163.7 s, p < 0.001). Cohen's kappa was excellent (>0.75; p < 0.001) and Cronbach's alpha was 0.994., Conclusion: FLAIRfusion provides reliable detection of newly developed MS lesions along with strong interreader agreement across all levels of expertise in 35 s of reading time.