This article brings knowledge of the settings of the Ublox ZED-F9P device, which will allow to use this receiver to build-up a simple low-cost GNSS device. The settings for the rover and base station functions are different and need to be done very accurately. Next we present a wiring scheme of the Ublox receiver with the Arduino system which as a whole device will enable the full functionality of the GNSS receiver as it is known from commonly used commercial devices. Ublox receiver was tested on accuracy and solution stability, results are shown in the article. At our department we create devices usable for teaching students as users or developers.

This paper presents an efficient and accurate method for detecting flat roof leaks using a combination of unmanned aerial vehicles (UAVs) and deep learning. The proposed method utilizes a DJI M300 drone equipped with RGB and thermal cameras to capture high-resolution images of the roof. These images are then processed to create orthomosaics and digital elevation models (DEMs). A deep learning model based on the U-NET architecture is then used to segment the roof into different classes, such as PVC foil, windows, and sidewalks. Finally, the damaged insulation is identified by analyzing the temperature distribution within the PVC foil segments. The proposed method has several advantages over traditional inspection methods. It is much faster and more efficient. A UAV can collect images of a large roof in a matter of minutes, while traditional methods can take several days or weeks. The orthomosaics and temperature maps generated by the UAV are much more detailed than the images that can be collected by a human inspector. Third, the UAV-based system is safer. The UAV can collect images of the roof without the need for a human inspector to climb onto the roof, which can be dangerous. The results of this study show that the proposed method is an effective and accurate way to detect flat roof leaks. The deep learning model was able to achieve an overall accuracy of 95% in segmenting the roof into different classes. The method was also able to identify damaged insulation with a high degree of accuracy.

Spherical cameras are being used more frequently in surveying because of their low cost and possibility to process spherical images with conventional SfM methods. Fish-eye lenses are now frequently found on modern 360° cameras, allowing for the capture of the entire scene and subsequent model reconstitution by a non-photogrammetrist specialist. Gyrospherics are a feature of cameras that ensure image stabilization to reduce blur when the camera is moving. This feature allows it to capture moving scenes in time-lapse mode. However, two main factors - hardware parameters and software algorithms - affect the quality of the images that are captured. The 360° camera design allows for a variety of data processing methods by SfM. These images were created using multiple image sensors and lenses from each 360° camera. These images can be processed individually or by applying rules to define relationships between images. Also, images from cameras can be stitched and processed with a spherical camera model. In this paper is proposed processing methods of data from 360° cameras and estimated accuracy of each method.

The article delves into the application of laser scanning within the realm of engineering, concentrating its examination on rotary kilns. Through an in-depth case study, this article meticulously assesses the advantages inherent in utilising laser scanning technology and draws comparisons with conventional measurement methods. The overarching objective of the investigation is to unravel the intricate relationship between the deterioration of the rotary kiln liners and the ovality of the underlying steel structure. By meticulously analysing these aspects, this article seeks to contribute valuable insights into the understanding of this complex interplay in the context of engineering practices. As a measurement apparatus, a terrestrial laser scanner was used. The interior and exterior of the rotary kiln were measured. The primary focus object was inner-lining loss and the geometric characteristics of the cylindrical shells. The research uncovered significant disparities in inner lining loss between the sections. A correlation was found between the ovality and elimination of the inner lining. Due to the hypothesis of constant inner lining loss in the middle of the rotary kiln, the investigation found that the loss of brick lining was less than the value reported from the wells. This study offers significant information on the maintenance and repair strategies for rotary kilns, which have the potential to increase their efficiency and useful life.

Cooling towers with natural draft are mainly used in large power or chemical plants. The shape of cooling tower is rotation hyperboloid in order to gain optimal performance of natural draft. The height of cooling tower is designed by maximum cooling capacity. The smallest cooling tower has 35m (Heerlen – NL 1914) and the biggest tower reaches the height of 200m (Niederaussen – D 1998). Perfect geometry of rotation hyperboloid must be respected as a thin thickness of tower is susceptible to vibration and gusts of wind. This study offers a comparison of measured towers shape and model from blueprints. Data were obtained by laser scanning and close-range photogrammetry. During the measurement the cooling tower was inoperative, so it was possible to measure even inside the cooling tower. Model was processed in Reality Capture software which allows to combine laser scan and photogrammetry data. Final model was used for comparison with mathematical model of rotation hyperboloid, shell thickness analysis and damage detection of outer tower shell. Geometrical analyses processed in Cloud Compare software show the difference of reference model against tested model and the result is difference model. Mathematical model of rotation hyperboloid was represented by mesh, and it was created in Blender software. Texture of outer tower shell was projected by cylindrical formula to map for damage detection. Damages like crack and exposed reinforcement were visible on outer tower shell. Summarization of these damages allows determination cooling tower liveliness. Damage detection analysis was obtained by deep learning by CNN model and OBIA in eCognition software.