In this letter, we propose a novel depth image completion technique based on sparse consecutive measurements of a non-repetitive circular scanning (NRCS) Lidar, demonstrating the capabilities of a new, compact, and accessible sensor technology for dense range mapping of highly dynamic scenes. Our deep network called ST-DepthNet is composed of a spatio-temporally (ST) extended U-Net architecture, which accepts a very sparse range data sequence as input and produces a dense depth image stream of the same field-of-view ensuring a high level of spatial details and accuracy. For evaluation, we have constructed a new urban dataset, that – to our best knowledge as the first open Benchmark in this field – comprises various simulated and real-world NRCS Lidar data samples, allowing us to simultaneously train our model on synthetic data with Ground Truth, and to validate the result via real NRCS Lidar measurements. Using this new dataset, we have shown the superiority of our method against a densified depth map obtained from the raw sensor stream, and against two independent state-of-the-art deep-learning based Lidar-only depth completion methods.

Artificial intelligence (AI) has been widely used in medicine, including orthodontics. The aim of this study was to investigate the training process of a cascaded Convolutional Neural Network (CNN), built for landmark detection on various qualities of lateral cephalograms and to determine the speed, reliability and clinical accuracy of an algorithm for orthodontic diagnosis. The CNN model was trained on a total of 1600 lateral cephalograms. After each training datasets (input of 400, 800, 1200, 1600 images) were added, the model was evaluated on a test set containing 78 images of varying quality. We measured the accuracy of AI-based landmark detection by statistical analysis of intra- and interexaminer distance errors, as well as examiner versus model predictions, furthermore by prognosis of consecutive diagnostic failures. There was a clear improvement in time efficiency (5.25 min), and substantial improvements were observed during the training process. In terms of accuracy, based on Euclidean distance error measurements, the best model provided more consistent dot tracing than two different examiners or the same examiner on two different occasions. Angular (0.05°–1.86°) and proportional (3.14%) errors, measured by the best model, were considered clinically acceptable. The application of a proper AI-algorithm for orthodontic cephalometric analysis results in lower variability between models than the variability observed among experts. AI predictions supported the examiners in finding the correct location of the specific landmarks more accurately and in less time as the training of the automatic prediction model improved. Further research could investigate the therapeutic consequences.

In this paper we propose a novel approach for upgrading real time 3D dynamic object detection methods operating on rotating multi-beam (RMB) Lidar measurements using 3D background city maps stored in new generation geographic information systems (GIS) and previously detected dynamic objects propagated by tracking. First, we apply a state-of-the-art object detection method and distinguish the predicted dynamic object candidates and the remaining static regions of the current Lidar measurement. Next we find an optimal transformation between the static part of the RMB Lidar measurements and the background city map using a multimodal point cloud registration algorithm operating in the Hough space. After the accurate alignment, we filter false-positively detected object candidates in the RMB Lidar data based on the map. To find additional objects missed by the object detector on the current measurement, we apply a Kalman-filter based object tracking. Hereby we first predict the current state of the previously detected and tracked objects. Next, we apply a Hungarian matcher based assignment between the tracked and the current objects and update the object list according to the result. For better accuracy, we keep all predictions through a couple of frames. We evaluated our method qualitatively and quantitatively in crowded urban scenes of Budapest, Hungary, and the results showed that with background map based filtering we can achieve a 26,52% improvement detecting vehicles and 9,38% for pedestrians in precision, while via tracking, a 12,84% improvement for vehicles and 14,34% for pedestrians in recall against the state-of-the-art object detection method relying purely on a single Lidar time frame.

This study presents an operational approach to atmospheric wind profiling using a purpose-built meteorological uncrewed aerial vehicle (UAV) and an orientation-based wind estimation method that does not rely on dedicated onboard anemometers. The quadrotor platform, designed and developed by our team, has a maximum take-off mass of 2.45 kg and is capable of acquiring vertical atmospheric profiles up to 3000 m under a wide range of weather conditions. Within the framework of the World Meteorological Organization’s (WMO) global demonstration campaign for evaluating the use of uncrewed aircraft systems in operational meteorology and associated field activities, twelve vertical wind profiles were collected in parallel with radiosonde observations. UAV-based wind estimates were evaluated against radiosonde data using the WMO OSCAR (Observing Systems Capability Analysis and Review) performance framework. Across most wind speed regimes, the central 50% of UAV–radiosonde wind speed differences remain within OSCAR threshold requirements, indicating operationally relevant accuracy. Systematic deviations are physically interpretable and arise primarily in strongly sheared boundary-layer flows. A representative low-level jet case is used as a stress test, demonstrating that the UAV system remains safe and that wind estimates remain reliable even under extreme wind conditions, supporting robust performance in less demanding regimes. These results establish UAV-based wind profiling as a viable and complementary observing technique in the lower atmosphere and provide a practical pathway toward high-resolution, operational boundary-layer wind measurements.