Search Results (36)

Background/Objectives: Brainstem ischemic strokes comprise 10% of ischemic strokes and are challenging to diagnose due to small lesion size and complex presentations. Diffusion-weighted imaging (DWI) is crucial for detecting ischemia, yet it can miss small lesions, especially when only axial slices are employed. This study investigated whether ischemic lesions visible in a single imaging plane correspond to smaller volumes and whether coronal DWI enhances detection compared to axial DWI alone. Methods: This retrospective single-center study examined 134 patients with brainstem ischemic strokes between December 2018 and November 2023. All patients underwent axial and coronal DWI. Clinical data, NIH Stroke Scale (NIHSS) scores, and modified Rankin Scale (mRS) scores were recorded. Diffusion-restricted lesion volumes were calculated using multiple models (planimetric, ellipsoid, and spherical), and lesion visibility per imaging plane was analyzed. Results: Brainstem ischemic strokes were detected in 85.8% of patients. Coronal DWI alone identified 6% of lesions that were undetectable on axial DWI; meanwhile, axial DWI alone identified 6.7%. Combining both improved overall sensitivity to 86.6%. Ischemic lesions visible in only one plane were significantly smaller across all volume models. Higher NIHSS scores were strongly correlated with larger diffusion-restricted lesion volumes. Coronal DWI correlated better with clinical severity than axial DWI, especially in the midbrain and medulla. Conclusions: Coronal DWI significantly improves the detection of small brainstem infarcts and should be incorporated into routine stroke imaging protocols. Infarcts visible in only one plane are typically smaller, yet still clinically relevant. Combined imaging enhances diagnostic accuracy and supports early and precise intervention in posterior circulation strokes. Full article

Rail systems are essential for economic growth and regional connectivity, but aging infrastructures face challenges from increased demand and environmental factors. Traditional inspection methods, such as visual inspections, are inefficient and costly and pose safety risks. Unmanned Aerial Vehicles (UAVs) have become a viable alternative to rail mapping and monitoring. This study presents a robust method for the 3D extraction of rail tracks from UAV-based aerial imagery. The approach integrates YOLOv8 for initial detection and segmentation, photogrammetry for 3D data extraction and computer vision techniques with a Multiview approach to enhance accuracy. The tool was tested in a real-world complex scenario. Errors of 2 cm and 4 cm were obtained for planimetry and altimetry, respectively. The detection performance and metric results show a significant reduction in errors and increased precision compared to intermediate YOLO-based outputs. In comparison to most image-based methodologies, the tool has the advantage of generating both accurate altimetric and planimetric data. The generated data exceed the requirements for cartography at a scale of 1:500, as required by the Spanish regulations for photogrammetric works for rail infrastructures. The tool is integrated into the open-source QGIS platform; the tool is user-friendly and aims to improve rail system maintenance and safety. Full article

Remote sensing from unoccupied aerial systems (UASs) has witnessed exponential growth. The increasing use of imaging spectroscopy sensors and RGB cameras on UAS platforms demands accurate, cross-comparable multi-sensor data. Inherent errors during image capture or processing can introduce spatial offsets, diminishing spatial accuracy and hindering cross-comparison and change detection analysis. To address this, we demonstrate the use of an optical flow algorithm, eFOLKI, for co-registering imagery from two pushbroom imaging spectroscopy sensors (VNIR and NIR/SWIR) to an RGB orthomosaic. Our study focuses on two ecologically diverse vegetative sites in Tasmania, Australia. Both sites are structurally complex, posing challenging datasets for co-registration algorithms with initial georectification spatial errors of up to 9 m planimetrically. The optical flow co-registration significantly improved the spatial accuracy of the imaging spectroscopy relative to the RGB orthomosaic. After co-registration, spatial alignment errors were greatly improved, with RMSE and MAE values of less than 13 cm for the higher-spatial-resolution dataset and less than 33 cm for the lower resolution dataset, corresponding to only 2–4 pixels in both cases. These results demonstrate the efficacy of optical flow co-registration in reducing spatial discrepancies between multi-sensor UAS datasets, enhancing accuracy and alignment to enable robust environmental monitoring. Full article

Coastal defense has traditionally relied on hard infrastructures like breakwaters, dykes, and groins to protect harbors, settlements, and beaches from the impacts of longshore drift and storm waves. The prolonged exposure to wave erosion and dynamic loads of different nature can result in damage, deformation, and eventual failure of these infrastructures, entailing severe economic and environmental losses. Periodic post-construction monitoring is crucial to identify shape changes, ensure the structure’s stability, and implement maintenance works as required. This paper evaluates the performance and quality of the restitution products obtained from the application of UAV photogrammetry to the longest breakwater in the province of Cádiz, southern Spain. The photogrammetric outputs, an orthomosaic and a Digital Surface Model (DSM), were validated with in situ RTK-GPS measurements, displaying excellent planimetric accuracy (RMSE 0.043 m and 0.023 m in X and Y, respectively) and adequate altimetric accuracy (0.100 m in Z). In addition, the average enveloping surface inferred from the DSM allowed quantification of the deformation of the breakwater and defining of the deformation mechanisms. UAV photogrammetry has proved to be a suitable and efficient technique to complement traditional monitoring surveys and to provide insights into the deformation mechanisms of coastal structures. Full article

In airborne surveying, light detection and ranging (LiDAR) strip adjustment and image bundle adjustment are customarily performed as separate processes. The bundle adjustment is usually conducted from frame images, while using linear pushbroom (LP) images in the bundle adjustment has been historically challenging due to the limited number of observations available to estimate the exterior image orientations. However, data from these three sensors conceptually provide information to estimate the same trajectory corrections, which is favorable for solving the problems of image depth estimation or the planimetric correction of LiDAR point clouds. Thus, our purpose with the presented study is to jointly estimate corrections to the trajectory and interior sensor states in a scalable hybrid adjustment between 3D LiDAR point clouds, 2D frame images, and 1D LP images. Trajectory preprocessing is performed before the low-frequency corrections are estimated for certain time steps in the following adjustment using cubic spline interpolation. Furthermore, the voxelization of the LiDAR data is used to robustly and efficiently form LiDAR observations and hybrid observations between the image tie-points and the LiDAR point cloud to be used in the adjustment. The method is successfully demonstrated with an experiment, showing the joint adjustment of data from the three different sensors using the same trajectory correction model with spline interpolation of the trajectory corrections. The results show that the choice of the trajectory segmentation time step is not critical. Furthermore, photogrammetric sub-pixel planimetric accuracy is achieved, and height accuracy on the order of mm is achieved for the LiDAR point cloud. This is the first time these three types of sensors with fundamentally different acquisition techniques have been integrated. The suggested methodology presents a joint adjustment of all sensor observations and lays the foundation for including additional sensors for kinematic mapping in the future. Full article

Digital Elevation Models (DEMs) are a necessity for modelling many large-scale environmental processes. In this study, we investigate the potential of data from two spaceborne lidar altimetry missions, ICESat-2 and GEDI—with respect to their vertical accuracies and planimetric data collection patterns—as sources for rasterisation towards creating global DEMs. We validate the terrain measurements of both missions against airborne lidar datasets over three areas in the Netherlands, Switzerland, and New Zealand and differentiate them using land-cover classes. For our experiments, we use five years of ICESat-2 ATL03 data and four years of GEDI L2A data for a total of 252 million measurements. The datasets are filtered using parameter flags provided by the higher-level products ICESat-2 ATL08 and GEDI L3A. For all areas and land-cover classes combined, ICESat-2 achieves a bias of −0.11 m, an MAE of 0.43 m, and an RMSE of 0.93 m. From our experiments, we find that GEDI is less accurate, with a bias of 0.09 m, an MAE of 0.98 m, and an RMSE of 2.96 m. Measurements in open land-cover classes, such as “Cropland” and “Grassland”, result in the best accuracy for both missions. We also find that the slope of the terrain has a major influence on vertical accuracy, more so for GEDI than ICESat-2 because of its larger horizontal geolocation error. In contrast, we find little effect of either beam power or background solar radiation, nor do we find noticeable seasonal effects on accuracy. Furthermore, we investigate the spatial coverage of ICESat-2 and GEDI by deriving a DEM at different horizontal resolutions and latitudes. GEDI has higher spatial coverage than ICESat-2 at lower latitudes due to its beam pattern and lower inclination angle, and a derived DEM can achieve a resolution of 500 m. ICESat-2 only reaches a DEM resolution of 700 m at the equator, but it increases to almost 200 m at higher latitudes. When combined, a 500 m resolution lidar-based DEM can be achieved globally. Our results indicate that both ICESat-2 and GEDI enable accurate terrain measurements anywhere in the world. Especially in data-poor areas—such as the tropics—this has potential for new applications and insights. Full article

Shoho Castle Maps are maps of castle towns throughout Japan drawn by Kano School painters on the order of the shogun in 1644. The Shoho Castle Map of Takada, Joetsu City, Niigata Prefecture was used to visualize local distortions in historical maps and to scrutinize the mapmaking process. A novel method, geographically weighted bidimensional regression, was developed and applied to visualize the local distortions of the map. Exaggerated expressions by mapmakers that have not been identified in previous studies were revealed. That is, in addition to the castle being drawn enlarged, the town where the merchants and artisans lived was drawn larger than the castle. Therefore, the Takada Shoho Castle Map reflects mapmakers’ intentions, besides enlarging military facilities, which appear to have emphasized the pictorial composition of the map by placing the main gate to the castle at the center and drawing the map area evenly from the center in a well-balanced layout. Full article

In recent decades, an important number of regional and global digital elevation models (DEMs) have been released publicly. As a consequence, researchers need to choose between several of these models to perform their studies and to use these DEMs as third-party data to compute derived products (e.g., for orthorectification). However, the comparison of DEMs is not trivial. For most quantitative comparisons, DEMs need to be expressed in the same coordinate reference system (CRS) and sampled over the same grid (i.e., be at the same ground sampling distance with the same pixel-is-area or pixel-is-point convention) with heights relative to the same vertical reference system (VRS). Thankfully, many open tools allow us to perform these transformations precisely and easily. Despite these rigorous transformations, local or global planimetric displacements may still be observed from one DEM to another. These displacements or disparities may lead to significant biases in comparisons of DEM elevations or derived products such as slope, aspect, or curvature. Therefore, before any comparison, the control of DEM planimetric accuracy is certainly a very important task to perform. This paper presents the disparity analysis method enhanced to achieve a sub-pixel accuracy by interpolating the linear regression coefficients computed within an exploration window. This new method is significantly faster than oversampling the input data because it uses the correlation coefficients that have already been computed in the disparity analysis. To demonstrate the robustness of this algorithm, artificial displacements have been introduced through bicubic interpolation in an 11 × 11 grid with a 0.1-pixel step in both directionsThis validation method has been applied in four approximately 10 km × 10 km DEMIX tiles showing different roughness (height distribution). Globally, this new sub-pixel accuracy method is robust. Artificial displacements have been retrieved with typical errors (e_b) ranging from 12 to 20% of the pixel size (with the worst case in Croatia). These errors in displacement retrievals are not equally distributed in the 11 × 11 grid, and the overall error E_b depends on the roughness encountered in the different tiles. The second aim of this paper is to assess the impact of the bicubic parameter (slope of the weight function at a distance d = 1 of the interpolated point) on the accuracy of the displacement retrieval. By considering E_b as a quality indicator, tests have been performed in the four DEMIX tiles, making the bicubic parameter vary between −1.5 and 0.0 by a step of 0.1. For each DEMIX tile, the best bicubic (BBC) parameter b* is interpolated from the four E_b minimal values. This BBC parameter b* is low for flat areas (around −0.95) and higher in mountainous areas (around −0.75). The roughness indicator is the standard deviation of the slope norms computed from all the pixels of a tile. A logarithmic regression analysis performed between the roughness indicator and the BBC parameter b* computed in 67 DEMIX tiles shows a high correlation (r = 0.717). The logarithmic regression formula $\tilde{b}\left({\sigma }_{slope}\right)$ estimating the BBC parameter from the roughness indicator is generic and may be applied to estimate the displacements between two different DEMs. This formula may also be used to set up a future Adaptative Best BiCubic (ABBC) that will estimate the local roughness in a sliding window to compute a local BBC $\tilde{b}$. Full article

The combined use of Uncrewed Aerial Vehicles (UAVs) with an integrated Real Time Kinematic (RTK) Global Navigation Satellite System (GNSS) module and an external GNSS base station allows photogrammetric surveys with centimeter accuracy to be obtained without the use of ground control points. This greatly reduces acquisition and processing time, making it possible to perform rapid monitoring of landslides by installing permanent and clearly recognizable optical targets on the ground. In this contribution, we show the results obtained in the Ca’ Lita landslide (Northern Apennines, Italy) by performing multi-temporal RTK-aided UAV surveys. The landslide is a large-scale roto-translational rockslide evolving downslope into an earthslide–earthflow. The test area extends 60 × 10³ m² in the upper track zone, which has recently experienced two major reactivations in May 2022 and March 2023. A catastrophic event took place in May 2023, but it goes beyond the purpose of the present study. A total of eight UAV surveys were carried out from October 2020 to March 2023. A total of eight targets were installed transversally to the movement direction. The results, in the active portion of the landslide, show that between October 2020 and March 2023, the planimetric displacement of targets ranged from 0.09 m (in the lateral zone) to 71.61 m (in the central zone). The vertical displacement values ranged from −2.05 to 5.94 m, respectively. The estimated positioning errors are 0.01 (planimetric) and 0.03 m (vertical). The validation, performed by using data from a permanent GNSS receiver, shows maximum differences of 0.18 m (planimetric) and 0.21 m (vertical). These results, together with the rapidity of image acquisition and data processing, highlight the advantages of using this rapid method to follow the evolution of relatively rapid landslides such as the Ca’ Lita landslide. Full article

Due to the limitations of measurement equipment and the influence of factors such as the environment and target, measurement errors may occur during the data acquisition process of airborne LiDAR bathymetry (ALB). The refractive index of water is defined as the propagation ratio of the speed of light waves in a vacuum to that in water; this ratio influences not only the propagation speed of the laser pulse in water but also the propagation direction of the laser pulse entering water. Therefore, the influence of refractive index changes in water on the ALB errors needs to be analyzed. To this end, the principle of ALB is first briefly introduced. Then, the calculation method for the refractive index of water is described with Snell’s law and an empirical formula. Finally, the influence of refractive index changes on ALB errors is analyzed using the derived formula at the water–air interface and in the water column. The experimental results showed that in a constant elevation of 50 m for a bathymetric floor, the refractive index changes in water caused by temperature, salinity, and depth are less than 0.001. The maximum bathymetric error and maximum planimetric error caused by the refractive index changes at the water–air interface are 0.036 m and 0.015 m, respectively. The ALB errors caused by refractive index changes in the water column are relatively low, and the water column does not need to be layered to calculate the ALB errors. The influence of refractive index changes in water on the ALB error is minimal, accounting for only a small proportion of all bathymetric errors. Thus, it is necessary to determine whether the effect of the ALB error due to refractive index changes in water needs to be corrected based on the accuracy requirements of the data acquisition. This study and analysis can provide a reference basis for correcting ALB errors. Full article

Airborne laser scanning sensors are impressive in their ability to collect a large number of topographic points in three dimensions in a very short time thus providing a high-resolution depiction of complex objects in the scanned areas. The quality of any final product naturally depends on the original data and the methods of generating it. Thus, the quality of the data should be evaluated before assessing any of its products. In this research, a detailed evaluation of a LIDAR system is presented, and the quality of the LIDAR data is quantified. This area has been under-emphasized in much of the published work on the applications of airborne laser scanning data. The evaluation is done by field surveying. The results address both the planimetric and the height accuracy of the LIDAR data. The average discrepancy of the LIDAR elevations from the surveyed study area is 0.12 m. In general, the RMSE of the horizontal offsets is approximately 0.50 m. Both relative and absolute height discrepancies of the LIDAR data have two components of variation. The first component is a random short-period variation while the second component has a less significant frequency and depends on the biases in the geo-positioning system. Full article

Unmanned aerial vehicles (UAVs) can provide valuable spatial information products for many projects across a wide range of applications. One of the major challenges in this discipline is the quality of positioning accuracy of the resulting mapping products in professional photogrammetric projects. This is especially true when using low-cost UAV systems equipped with GNSS receivers for navigation. In this study, the influence of UAV flight direction and camera orientation on positioning accuracy in an urban area on the west bank of the Euphrates river in Iraq was investigated. Positioning accuracy was tested in this study with different flight directions and camera orientation settings using a UAV autopilot app (Pix4Dcapture software (Ver. 4.11.0)). The different combinations of these two main parameters (camera orientation and flight direction) resulted in 11 different flight cases for which individual planimetric and vertical accuracies were evaluated. Eleven flight sets of dense point clouds, DEMs, and ortho-imagery were created in this way to compare the achieved positional accuracies. One set was created using the direct georeferencing method (without using GCPs), while the other ten sets were created using the indirect georeferencing approach based on ground truth measurements of five artificially created GCPs. Positional accuracy was found to vary depending on the user-defined flight plan settings, despite an approximately constant flight altitude. However, it was found that the horizontal accuracy achieved was better than the vertical accuracy for all flight sets. This study revealed that combining multiple sets of images with different flight directions and camera orientations can significantly improve the overall positional accuracy to reach several centimeters. Full article

Cadastral marks constitute a dense source of information for topographical surveys required to update cadastral maps. Historically, in Italy, cadastral marks have been the cartographic network for the implementation of mapping updates. Different sources of cadastral marks can be used by cadastral surveyors. In recent years, the cadastre is moving toward a digital world, and with the advancement of surveying technology, GNSS CORS technology has emerged in the positioning of cadastral marks. An analysis of congruence among cadastral marks using GNSS CORS and official maps is missing. Thus, this work aims to analyze the positional accuracy of some cadastral marks, located in Palermo, Italy, with regard to the official maps produced by the cadastral bureau, the local cartography, and Google Earth maps. A survey of 60 cadastral marks was carried out by conventional GNSS NRTK procedures, with the lateral offset method due to their materialization (mostly building edges), which is not always directly detectable. The cadastral marks’ positioning was obtained from different maps: cadastral maps and related monographic files, numerical technical maps, and Google Earth maps, to check their coordinate congruence. A statistical approach was performed to check whether the distribution frequencies of the coordinate’s differences belonged to the bivariate normal distribution for the planimetric coordinates and the univariate normal distribution for the altimetric component. The results show that the hypothesis of a normal distribution is confirmed in most of the pairs, and specifically, most of the analyses indicate that the highest congruencies seem to characterize the coordinates determined by using the GNSS and with those that can be deduced by the numerical technical maps. The results obtained experimentally show centimetric accuracies obtained by the GNSS NRTK survey, in both the planimetric and altimetric components, while the accuracies obtained from the georeferencing of the cadastral maps show differences in the order of 0.4–0.8 m. Meanwhile, the differences resulting from comparing the technical cartography produced by the local authority and Google Earth maps show greater criticalities, with a metric order of magnitude. Full article

Traditional grain size determination in materials characterization involves microscopy images and a laborious process requiring significant manual input and human expertise. In recent years, the development of computer vision (CV) has provided an alternative approach to microstructural characterization with preliminary implementations greatly simplifying the grain size determination process. Here, an end-to-end workflow to measure grain size in microscopy images without any manual input is presented. Following the ASTM standards for grain size determination, results from the line intercept (Heyn’s method) and planimetric (Saltykov’s method) approaches are used as the baseline. A pre-trained holistically nested edge detection (HED) model is used for CV-based edge detection, and the results are further compared to the classic Canny edge detection method. Post-processing was performed using open-source image processing packages to extract the grain size. In optical microscope images, the pre-trained HED model achieves much higher accuracy than the Canny edge detection method while reducing the image processing time by one to two orders of magnitude compared to traditional methods. The effects of morphological operations on the predicted grain size accuracy are also explored. Overall, the proposed end-to-end convolutional neural network (CNN)-based workflow can significantly reduce the processing time while maintaining the same accuracy as the traditional manual method. Full article

Over the last two decades, Geiger-mode lidar (GML) systems have been developing rapidly in defense and commercial applications, demonstrating high point density and great collection efficiency. We presented a circular scanning GML system simulation model for performance prediction and developed a GML system for civilian mapping. The lidar system used an eye-safe fiber laser at 1545 nm coupled with a 64 × 64 pixels photon-counting detector array. A real-time data compression algorithm was implanted to reduce half of the data transmission rate and storage space compared to the uncompressing situation. The GML system can operate at aircraft above-ground levels (AGLs) between 0.35 km and 3 km, and at speeds in excess of 220 km/h. The initial flight tests indicate that the GML system can operate day and night with an area coverage of 366 km²/h. The standard deviations of the relative altimetric accuracy and the relative planimetric accuracy are 0.131 m and 0.152 m, respectively. The findings presented in this article guide the implementation of designing an airborne GML system and the data compression method. Full article