This study evaluates the effectiveness of augmented reality (AR), using the Microsoft™ HoloLens™ 2, for identifying and localizing PCB components compared to traditional PDF-based methods. Two experiments examined the influence of user expertise, viewing angles, and component sizes on accuracy and usability. The results indicate that AR improved identification accuracy and user experience for non-experts, although it was slower than traditional methods for experienced users. Optimal performance was achieved at 90° viewing angles, while accuracy declined significantly at oblique angles. Medium-sized components received the highest confidence scores, suggesting favorable visibility and recognition characteristics within this group, though further evaluation with a broader component distribution is warranted. Participant feedback highlighted the system’s intuitive interface and effective guidance but also noted challenges with marker stability, visual discomfort, and ergonomic limitations. These findings suggest that AR can enhance training and reduce errors in electronics manufacturing, although refinements in marker rendering and user onboarding are necessary to support broader adoption. This research provides empirical evidence on the role of AR in supporting user-centered design and improving task performance in industrial electronics workflows.

The fast-spreading wildfire engulfs the dense parched flora and all obstructions in its way, transforming a woodland into a volatile reservoir of combustible materials. Once ignited, fires can expand at a velocity of up to 23 km/h. As flames spread across vegetation and woodlands, they have the potential to become self-sustaining, propagating sparks and embers that can spawn smaller fires miles away. The proximity of the burning materials to the observer has a direct impact on the density of smoke produced by the fire. This relationship is crucial for fire management teams and emergency responders and helps them assess the severity of a fire, predict its behavior, and make informed decisions regarding evacuation measures, resource allocation, and the protection of affected communities and ecosystems. Drones are valuable tools in the fight against forest fires. They can capture high-resolution imagery, thermal imaging, and video footage, supplying insights into the properties, behavior, and direction of the fire. By employing classical image processing techniques, it is possible to analyze these images and promptly determine the extent of land cover affected.According to the Swedish Civil Contingencies Agency, more than 25000 ha of forest burned down during the period of 2012-2021, which resulted in severe damage costs. The presence of a reliable and easily accessible smoke detection and assessment tool could significantly reduce the impact of wildfires. This study utilizes low and mid-level image processing techniques to analyze the domain of wildfires, leveraging smoke properties to estimate the extent of land affected by the flames.

This study evaluates the accuracy of Microsoft HoloLens (Version 1) for small-scale industrial activities, comparingits measurements to ground truth data from a Kuka Robotics arm. Two experiments were conducted to assess its positiontracking capabilities, revealing that the HoloLens device is effective for measuring the position of dynamic objects with smalldimensions. However, its precision is affected by the velocity of the trajectory and its position within the device's field of view.While the HoloLens device may be suitable for small-scale tasks, its limitations for more complex and demanding applicationsrequiring high precision and accuracy must be considered. The findings can guide the use of HoloLens devices in industrialapplications and contribute to the development of more effective and reliable position-tracking systems.

Presently, digitalization is causing continuous transformation of industrial processes. However, it does pose challenges like spatially contextualizing data from industrial processes. There are various methods for calculating and delivering real-time location data. Indoor positioning systems (IPS) are one such method, used to locate objects and people within buildings. They have the potential to improve digital industrial processes, but they are currently under utilized. In addition, augmented reality (AR) is a critical technology in today’s digital industrial transformation. This article aims to investigate the use of IPS and AR in manufacturing, the methodologies and technologies employed, the issues and limitations encountered, and identify future research opportunities. This study concludes that, while there have been many studies on IPS and navigation AR, there has been a dearth of research efforts in combining the two. Furthermore, because controlled environments may not expose users to the practical issues they may face, more research in a real-world manufacturing environment is required to produce more reliable and sustainable results

Forest fires cause major damage to human habitats and forest ecosystems. Early detection may prevent serious consequences of fast fire spread. Although there are many smoke detection algorithms employed by various optical remote sensing systems, there is still a major misdetection of images containing fog. Fog exhibits similar visual characteristics to smoke. Furthermore, when monitoring dense forests many smoke detection algorithms would fail in robust recognition due to fog covering the trees at dawn. There have been more or less successful attempts to separate smoke from a fog in optical imagery however, these algorithms are strongly connected to a specific application area or use a semi-automatic approach. This work aims to propose a novel smoke and fog separation algorithm based on color space model calculation followed by rule-based shape analysis. In addition, the internal properties of the smoke candidate areas are examined for linear attenuation towards higher energy wavelength. Those areas are then investigated for internal shape properties such as convex hull and eccentricity. Several tests conducted on various high-resolution aerial images suggest that the system is effective in differentiating smoke and fog and thus considered to be robust in early fire detection in forest areas. 

ABSTRACT The goal of this study is to investigate how efficiently and effectively collapsed buildings ? due to the occurrence of a disaster ? can be localized by a general crowd. Two types of visualization parameters are evaluated in an online user study: (1) greyscale images (indicating height information) versus true colours; (2) variation in the vertical viewing angle (0°, 30° and 60°). Additionally, the influence of map use expertise on how the visualizations are interpreted, is investigated. The results indicate that the use of the greyscale image helps to locate collapsed buildings in an efficient and effective manner. The use of the viewing angle of 60° is the least appropriate. A person with a map use expertise will prefer the greyscale image over the colour image. To confirm the benefits of the use of three-dimensional visualizations and the use of the colour image, more research is needed.

A user study is conducted to evaluate the efficiency and effectiveness of two types of visualizations to identify damage sites in case of disaster. The test consists out 36 trials (18 for each visualisation) and in each trial an area of 1x1km, located in Ghent, is displayed on a screen. This image shows the combined height information from before and after the disaster. The first visualisation, page flipping, is based on greyscale images with height information from the pre- and post-disaster situation between which users can switch manually. The second visualisation, difference image, is a result of subtracting the heights (before versus after) and assigning a blue-white-red colour ramp. In order to simulate the urgency with which the data is captured, systematic and random imperfections are introduced in the post-disaster data. All participants’ mouse and key interactions are logged, which is further complemented by the registration of their eye movements. This give insights the visualizations’ efficiency, effectiveness and the overall search strategies of the participants.

 A user study is conducted to evaluate the efficiency and effectiveness of two types of visualizations to identify damages sites in case of disaster. The test consists out 36 trials (18 for each visualisation) and in each trial an area of 1×1km, located in Ghent, is displayed on a screen. This image shows the combined height information from before and after the disaster. The first visualisation, page flipping, is based on greyscale images with height information from the pre- and post-disaster situation between which users can switch manually. The second visualisation, difference image, is a result of subtracting the heights (before versus after) and assigning a blue-white-red colour ramp. In order to simulate the urgency with which the data is captured, systematic and random imperfections are introduced in the post-disaster data. All participants’ mouse and key interactions are logged, which is further complemented by the registration of their eye movements. This give insights the visualizations’ efficiency, effectiveness and the overall search strategies of the participants.

In the context of urban planning, it is very important to estimate the nature of the roof of every building and, in particular, to make the difference between flat roofs and gable ones. This analysis is necessary in seismically active areas. Also in the assessment of renewable energy projects such solar energy, the shape of roofs must be accurately retrieved. In order to perform this task automatically on a large scale, aerial photos provide a useful solution. The goal of this research project is the development of algorithm for accurate mapping of two different roof types in digital aerial images. The algorithm proposed in this paper includes several components: pre-processing step to reduce illumination differences of individual roof surfaces, statistical moments calculation and color indexing. Roof models are created and saved as masks with feature specific descriptors. Masks are then used to mark areas that contain elements of the different roof types (e.g. gable and hip). The final orthophoto visualize an accurate position of each of the roof types. The result is evaluated using precision recall method.

This paper proposes an evaluation of data acquired with various sensors and used in coastal water segmentation applications. Correct monitoring of coastal changes in dynamic coastal environments strongly depends on accurate and frequent detection of shoreline position. Automatic shoreline delineation methods are preferable, especially in terms of time, cost, labor intensiveness and difficulties of in-situ measurements. Two main issues have been encountered within this application field, the quality of data and the segmentation algorithms. In this work, potential benefits of various data sources including optical and active sensors for extraction of shorelines have been investigated. The goal with shoreline detection from digital data sources is to obtain information as efficiently as possible and as reliably as necessary. Starting with that observation the paper discusses the effectiveness of coastal information extraction provided different data sources. This question is especially important to address since we observe a fast development of high spatial resolution data acquisition. There are many of segmentation algorithms described in the field of image processing and yet there is currently no single theory or method, no universal segmentation framework, that can be applied on all images to precisely and robustly extract shorelines. Nether there is a uniform standard for the assessment of segmentation results, and this process still largely relies on visual analysis and personal judgment. Out of myriads of image segmentation algorithms, we chose the most frequently and successfully applied within the application field and considering the data sources. In optical sensor data cases, the most frequently used methods are NDWI (Normalized Difference Water Index) and thresholding techniques. We do not aim to create yet another method to segment out the particular objects from remotely sensed data and then tailor it to work efficiently on that data set. Instead, we evaluate the data quality regarding the given application field. The case study is carried out on a 10 km coastal stretch facing the Baltic Sea (Sweden) and belonging to the Municipality of Gävle. In citu measurements were acquired to evaluate the extracted coastal lines and comparisons with reference were performed based on the average mean distance. A conclusion is done regarding the most reliable data source for this particular application of shoreline delineation.