Nowadays, the public service practice applicability of drones and remote sensing sensors is being explored in almost all industrial and military areas. In the present research, in collaboration with different universities, we investigate the applicability of drones in airport procedures, assessing the various potential applications. By exploiting the data from remote sensing sensors, we aim to develop methodologies that can assist airport operations, including managing the risk of wildlife threats to runway safety, infrastructure maintenance, and foreign object debris (FOD) detection. Drones equipped with remote sensing sensors provide valuable insight into surface diagnostics, helping to assess aprons, taxiways, and runways. In addition, drones can enhance airport security with effective surveillance and threat detection capabilities, as well as provide data to support existing air traffic control models and systems. In this paper, we aim to present our experience with the potential airport applications of UAV high-resolution RGB, thermal, and LiDAR sensors. Through interdisciplinary collaboration and innovative methodologies, our research aims to revolutionize airport operations, safety, and security protocols, outlining a path toward a safer, more efficient airport ecosystem.

Driving is based on effective navigation. When using a navigation device, the user interface, the amount and quality of the underlying data and its representation all effect the quality of navigation. This study evaluates whether drivers in three different countries consider these devices to be useful and what functionality they would prefer. An online questionnaire was used to assess built-in navigation systems. The findings from 213 respondents show that current car GPSs are overloaded with features. Regardless of country, drivers simply require more basic functionality in the interface. It was also noted that the embedded functions in these devices are not fully utilized. In addition, many people use the navigation service to enter a new address while the car is moving. It may be worth examining how this option can be better implemented.

Today, integration into automated systems has become a priority in the development of remote sensing sensors carried on drones. For this purpose, the primary task is to achieve real-time data processing. Increasing sensor resolution, fast data capture and the simultaneous use of multiple sensors is one direction of development. However, this poses challenges on the data processing side due to the increasing amount of data. Our study intends to investigate how the running time and accuracy of commonly used image classification algorithms evolve using Altum Micasense multispectral and thermal acquisition data with GSD = 2 cm spatial resolution. The running times were examined for two PC configurations, with a 4 GB and 8 GB DRAM capacity, respectively, as these parameters are closer to the memory of NRT microcomputers and laptops, which can be applied “out of the lab”. During the accuracy assessment, we compared the accuracy %, the Kappa index value and the area ratio of correct pixels. According to our results, in the case of plant cover, the Spectral Angles Mapper (SAM) method achieved the best accuracy among the validated classification solutions. In contrast, the Minimum Distance (MD) method achieved the best accuracy on water surface. In terms of temporality, the best results were obtained with the individually constructed decision tree classification. Thus, it is worth developing these two directions into real-time data processing solutions.

The need for car-related navigation and the appearance of the vehicles themselves are much the same age – of course. In the early days, paper maps and personal questioning were solved this problem. Nearly 100 years ago, the first built-in car GPS was released – and this is an innovation that is still an important part of car design. Planning the user interface is a very exciting issue, because only in a few seconds the display needs to provide the user with the right amount of information. In this research our goal was to assess Hungarians driving and built-in GPS using habits and based on this try to suggest the “perfect” interface. A questionnaire was made with 19 or 46 questions and more than 1000 respondents completed it. Because of the high number of questions only selected diagrams are represented. Some answers have also been subjected to different statistical tests.

The use of UAVs to map ionising radiation resulting from radioactive decay is gaining popularity among researchers due to its efficiency and safety. Many studies have been conducted, most of them using expensive sensors. The present research aims to investigate the applicability of an affordable radiation detector in areas where anomalies from natural sources occur. In this research, we use a DJI Matrice 210 V2 RTK quadcopter equipped with a cost-effective Safecast bGeigie Nano Kit radiation sensor to take measurements at different altitudes above ground. We convert these data into GIS-compatible formats and produce accurate isoline maps using the Minimum Curvature interpolation technique. The results show that while the radiation intensity decreases with height, the anomaly was visible but less detailed at all heights investigated. In addition, the study highlights the significant differences in position measurements between RTK GNSS and autonomous GNSS measurements that affect the accuracy of the data. The results will contribute to a more accurate determination of the radiation extent and, thus, to maintaining safety, as well as assisting in emergency surveys and environmental monitoring.