Aiming at the problems that the clock bias prediction model of the Wavelet Neural Network (WNN) is greatly affected by the selection of network parameters, and the Particle Swarm Optimization Wavelet Neural Network is prone to fall into local optima and has insufficient convergence efficiency in clock bias prediction, a short-term clock bias prediction model for BDS-3 based on the Rime Optimization Algorithm (RIME)-optimized Wavelet Neural Network is proposed. Firstly, the specific steps of the WNN model based on the RIME optimization algorithm in clock bias prediction are elaborated in detail. Then, the stability characteristics and training efficiency of the RIME optimization algorithm during the optimization stage are analyzed to determine the population size that suits the characteristics of clock bias data. Finally, using the BDS-3 clock bias data provided by the Wuhan University Data Center, short-term clock bias prediction experiments with durations of 1 h, 3 h, and 6 h are carried out. The experimental results show that in the 6h prediction, the average prediction accuracy of the RIME-WNN model is better than 0.1 ns, which is 93.92%, 88.35%, and 48.11% higher than that of the Quadratic Polynomial model, the Grey Model (GM(1,1)), and the PSO-WNN model, respectively. In addition, when the RIME-WNN model predicts different types of Beidou satellites, the maximum difference in the Root Mean Square Error (RMSE) is relatively smaller, which fully demonstrates that the model has a wide and good accuracy adaptability when predicting various types of Beidou satellites.
Satellite-based data can provide continuous aerosol observations but suffer from significant uncertainties across various regions. Transfer learning improves model generalization, yet its application in atmospheric research remains limited. Here, we introduce an innovative framework for retrieving global aerosol optical depth (AOD) which named the Aerosol domain-Adaptive Network (AAdaN). The framework utilizes a neural network to estimate mutual information, and aligns spatial covariate shift via a transfer loss term. Then, we assess the retrieval potential in unknown scenarios using independent land cover type, and the proposed model demonstrates satisfactory results. The cross-validation shows strong agreement with in-situ measurements, both in sample-based and site-based evaluations. Specifically, the site-based ten-fold cross-validation of our AOD retrievals indicate that all accuracy metrics are satisfactory, with a Pearson correlation of 0.766 and a Root-Mean-Square Error of 0.118, and that about 76.05 % of the retrievals meet the expected error criteria [±(0.05 + 20 %)]. Additionally, the proposed AAdaN achieves stable, high-accuracy aerosol retrievals across various surface and atmospheric conditions, and can generate spatially continuous AOD distributions. This study significantly improves spatial generalization and offers valuable insights for future model development.
Reliable pose information is essential for many applications, such as for navigation or surveying tasks. Though GNSS is a well-established technique to retrieve that information, it often fails in urban environments due to signal occlusion or multi-path effects. In addition, GNSS might be subject to jamming or spoofing, which requires an alternative, complementary positioning method. We introduce a visual localization method which employs building models according to the CityGML standard. In contrast to the most commonly used sources for scene representation in visual localization, such as structure-from-motion (SfM) points clouds, CityGML models are already freely available for many cites worldwide, do not require a large amount of memory and the scene representation database does not have to be generated from images. Yet, 3D models are rarely used because they usually lack properties such as texture or only contain general geometric structures. Our approach utilizes the boundary representation (BREP) of the CityGML models in Level of Detail (LOD) 2 and the geometry of the query image scene from extracted straight line segments. We investigate how we can use an energy function to determine the quality of the correspondence between the line segments of the query image and the projected line segments of the CityGML model based on a specific camera pose. This is then optimized to estimate the camera pose of the query image. We show that a rough estimation of the camera pose is possible purely via the distribution of the line segments and without prior calculation of features and their descriptors. Furthermore, many possibilities and approaches for improvements remain open. However, if these approaches are taken into account, we expect CityGML models to be a promising option for scene representation in visual localization.
Spatially referenced and geometrically accurate laser scanning is essential for the safety monitoring of an underground mine. However, the spatial inconsistency of point clouds collected by heterogeneous platforms presents challenges in achieving seamless fusion. In our study, the terrestrial and handheld laser scanning (TLS and HLS) point cloud registration method based on the coarse-to-fine strategy is proposed. Firstly, the point features (e.g., target spheres) are extracted from TLS and HLS point clouds to provide the coarse transform parameters. Then, the fine registration algorithm based on identical area extraction and improved 3D normal distribution transform (3D-NDT) is adopted, which achieves the datum unification of the TLS and HLS point cloud. Finally, the roughness is calculated to downsample the fusion point cloud. The proposed method has been successfully tested on two cases (simulated and real coal mine point cloud). Experimental results showed that the registration accuracy of the TLS and HLS point cloud is 4.3 cm for the simulated mine, which demonstrates the method can capture accurate and complete spatial information about underground mines.
Ziggah Yao Yevenyo, Mantey Saviour, Laari Prosper Basommi
Modern surveying practice has embraced the use of Global Navigation Satellite System (GNSS) technology due to its attainable precision and uncomplicated functionality. The adoption of this technology has therefore necessitated the transformation of coordinates between satellite-based and classical geodetic reference datums. It is known that the 3D similarity-based transformation models are the most widely used in the literature. However, one major limitation of such models is the representation of point rotations in space using Euler angles connected to X, Y, and Z-axes, which often leads to matrix singularities. To overcome this mathematical inconvenience, the dual quaternion is proposed. This paper implements the dual quaternion algorithm to transform coordinates between the World Geodetic System 1984 (WGS84) and Ghana War Office 1926. To perform the transformation, 31 common points were divided into two parts: reference and check points. The reference points, consisting of 24 common points that are evenly distributed across Ghana, were used to derive the transformation parameters. The remaining 7 points were used to evaluate the derived transformation parameters. The results confirmed that the coordinates transformed by the dual quaternion algorithm are in average agreement with the measured coordinates, with precision and accuracy levels of about 0.580 m and 1.023 m. The obtained results follow the Bursa-Wolf model that is already used by the Ghana Survey and Mapping Division to perform 3D transformations. Hence, the results satisfy cadastral applications, geographic information works, reconnaissance, land information system works and small-scale topographic surveys in Ghana.
Marina Viličić, Emilia Domazet, Martina Triplat Horvat
This article presents the procedure for determining numerical scales based on the graphic scales drawn to process the graphic material in Volume VII of the Valvasor collection. To calculate the numerical scales, the miles drawn on the maps and their lengths in relation to one degree of the meridian were studied. A total of 22 different miles were drawn on the maps studied, of which the German mile was the most common. After calculating the numerical scales, it was found that the largest scale of the maps examined was 1:220,000 and the smallest was 1:11,200,000.
Edyta Puniach, Wojciech Gruszczyński, Paweł Ćwiąkała
et al.
This study compared classifiers that differentiate between urbanized and non-urbanized areas based on unmanned aerial vehicle (UAV)-acquired RGB imagery. The tested solutions included numerous vegetation indices (VIs) thresholding and neural networks (NNs). The analysis was conducted for two study areas for which surveys were carried out using different UAVs and cameras. The ground sampling distances for the study areas were 10 mm and 15 mm, respectively. Reference classification was performed manually, obtaining approximately 24 million classified pixels for the first area and approximately 3.8 million for the second. This research study included an analysis of the impact of the season on the threshold values for the tested VIs and the impact of image patch size provided as inputs for the NNs on classification accuracy. The results of the conducted research study indicate a higher classification accuracy using NNs (about 96%) compared with the best of the tested VIs, i.e., Excess Blue (about 87%). Due to the highly imbalanced nature of the used datasets (non-urbanized areas constitute approximately 87% of the total datasets), the Matthews correlation coefficient was also used to assess the correctness of the classification. The analysis based on statistical measures was supplemented with a qualitative assessment of the classification results, which allowed the identification of the most important sources of differences in classification between VIs thresholding and NNs.
Ruslan Suleymanov, Azamat Suleymanov, Gleb Zaitsev
et al.
Traditional land-use systems can be modified under the conditions of climate change. Higher air temperatures and loss of productive soil moisture lead to reduced crop yields. Irrigation is a possible solution to these problems. However, intense irrigation may have contributed to land degradation. This research assessed the ameliorative potential of soil and produced large-scale digital maps of soil properties for arable plot planning for the construction and operation of irrigation systems. Our research was carried out in the southern forest–steppe zone (Southern Ural, Russia). The soil cover of the site is represented by agrochernozem soils (Luvic Chernozem). We examined the morphological, physicochemical and agrochemical properties of the soil, as well as its heavy metal contents. The random forest (RF) non-linear approach was used to estimate the spatial distribution of the properties and produce maps. We found that soils were characterized by high organic carbon content (SOC) and neutral acidity and were well supplied with nitrogen and potassium concentrations. The agrochernozem was characterized by favorable water–physical properties and showed good values for water infiltration and moisture categories. The contents of heavy metals (lead, cadmium, mercury, cobalt, zinc and copper) did not exceed permissible levels. The soil quality rating interpretation confirms that these soils have high potential fertility and are convenient for irrigation activities. The spatial distribution of soil properties according to the generated maps were not homogeneous. The results showed that remote sensing covariates were the most critical variables in explaining soil properties variability. Our findings may be useful for developing reclamation strategies for similar soils that can restore soil health and improve crop productivity.
<p>Digital elevation models (DEMs) are currently one of the most widely used data sources in glacier thickness change research, due to the high spatial resolution and continuous coverage. However, raw DEM data are often misaligned with each other, due to georeferencing errors, and a co-registration procedure is required before DEM differencing. In this paper, we present a comparative analysis of the two classical co-registration methods proposed by Nuth and Kääb (2011) and Rosenholm and Torlegard (1988). The former is currently the most commonly used method in glacial studies, while the latter is a seminal work in the photogrammetric field that has not been extensively investigated by the cryosphere community. Furthermore, we also present a new residual correction method using a generalized additive model (GAM) to eliminate the remaining systematic errors in DEM co-registration results. The performance of the two DEM co-registration methods and three residual correction algorithms (the GAM-based method together with two parametric-model-based methods) was evaluated using multiple DEM pairs from the Greenland Ice Sheet and mountain glaciers, including Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) DEMs, ZiYuan-3 (ZY-3) DEMs, the Shuttle Radar Topography Mission (SRTM) DEM, and the Copernicus DEM. The experimental results confirm our theoretical analysis of the two co-registration methods. The method of Rosenholm and Torlegard has a greater ability to remove DEM misalignments (an average of 4.6 % and 13.7 % for the test datasets from Greenland Ice Sheet and High Mountain Asia, respectively) because it models the translation, scale, and rotation-induced biases, while the method of Nuth and Kääb considers translation only. The proposed GAM-based method performs statistically better than the two residual correction methods based on parametric regression models (high-order polynomials and the sum of the sinusoidal functions). A visual inspection reveals that the GAM-based method, as a non-parametric regression technique, can capture complex systematic errors in the DEM co-registration residuals.</p>
Abstract Magnetotelluric (MT) field data contain natural electromagnetic signals and artificial noise sources (instrumental, anthropogenic, etc.). Not all available time-series data contain usable information on the electrical conductivity distribution at depth with a low signal-to-noise ratio. If variations in the natural electromagnetic signal increase dramatically in a geomagnetic storm, the signal-to-noise ratio increases. A more reliable impedance may be obtained using storm data in a noisy environment. The field datasets observed at mid-latitudes were used to investigate the effect of geomagnetic storms on MT impedance quality. We combined the coherence between the electric and magnetic fields and the result of the MT sounding curve to evaluate the MT impedance quality across all periods and combined the phase difference among the electric and magnetic fields, the polarization direction, and the hat matrix to discuss the data quality for a specific period simultaneously. The case studies showed that the utilization of the data observed during the geomagnetic storm could overcome the local noise and bring a more reliable impedance. Graphical Abstract
Abstract Accurate positioning and navigation play a vital role in vehicle-related applications, such as autonomous driving and precision agriculture. With the rapid development of Global Navigation Satellite Systems (GNSS), Precise Point Positioning (PPP) technique, as a global positioning solution, has been widely applied due to its convenient operation. Nevertheless, the performance of PPP is severely affected by signal interference, especially in GNSS-challenged environments. Inertial Navigation System (INS) aided GNSS can significantly improve the continuity and accuracy of navigation in harsh environments, but suffers from degradation during GNSS outages. LiDAR (Laser Imaging, Detection, and Ranging)-Inertial Odometry (LIO), which has performed well in local navigation, can restrain the divergence of Inertial Measurement Units (IMU). However, in long-range navigation, error accumulation is inevitable if no external aids are applied. To improve vehicle navigation performance, we proposed a tightly coupled GNSS PPP/INS/LiDAR (GIL) integration method, which tightly integrates the raw measurements from multi-GNSS PPP, Micro-Electro-Mechanical System (MEMS)-IMU, and LiDAR to achieve high-accuracy and reliable navigation in urban environments. Several experiments were conducted to evaluate this method. The results indicate that in comparison with the multi-GNSS PPP/INS tightly coupled solution the positioning Root-Mean-Square Errors (RMSEs) of the proposed GIL method have the improvements of 63.0%, 51.3%, and 62.2% in east, north, and vertical components, respectively. The GIL method can achieve decimeter-level positioning accuracy in GNSS partly-blocked environment (i.e., the environment with GNSS signals partly-blocked) and meter-level positioning accuracy in GNSS difficult environment (i.e., the environment with GNSS hardly used). Besides, the accuracy of velocity and attitude estimation can also be enhanced with the GIL method.
Takashi Maruyama, Kornyanat Hozumi, Guanyi Ma
et al.
Abstract A new technique was developed to estimate the ionospheric total electron content (TEC) from Global Navigation Satellite System (GNSS) satellite signals. The vertically distributed electron density was parameterized by two thin-shell layers (double-shell approach). The spatiotemporal variation of TEC (strictly speaking, partial electron content) associated with each shell was approximated by the functional fitting of spherical surface harmonics. The major improvements over the conventional single-shell approach were as follows: (1) the precise estimation of TEC was achieved; (2) the estimated TEC was less dependent on the choice of shell heights; and (3) the equatorial anomaly was captured more correctly. Furthermore, higher and lower shells exhibited a different pattern of local time vs latitude variation, providing information on the ionosphere–thermosphere dynamics.
Determining the altitude of side-scan sonar (SSS) above the seabed is critical to correct the geometric distortions in the sonar images. Usually, a technology named bottom tracking is applied to estimate the distance between the sonar and the seafloor. However, the traditional methods for bottom tracking often require pre-defined thresholds and complex optimization processes, which make it difficult to achieve ideal results in complex underwater environments without manual intervention. In this paper, a universal automatic bottom tracking method is proposed based on semantic segmentation. First, the waterfall images generated from SSS backscatter sequences are labeled as water column (WC) and seabed parts, then split into specific patches to build the training dataset. Second, a symmetrical information synthesis module (SISM) is designed and added to DeepLabv3+, which not only weakens the strong echoes in the WC area, but also gives the network the capability of considering the symmetry characteristic of bottom lines, and most importantly, the independent module can be easily combined with any other neural networks. Then, the integrated network is trained with the established dataset. Third, a coarse-to-fine segmentation strategy with the well-trained model is proposed to segment the SSS waterfall images quickly and accurately. Besides, a fast bottom line search algorithm is proposed to further reduce the time consumption of bottom tracking. Finally, the proposed method is validated by the data measured with several commonly used SSSs in various underwater environments. The results show that the proposed method can achieve the bottom tracking accuracy of 1.1 pixels of mean error and 1.26 pixels of standard deviation at the speed of 2128 ping/s, and is robust to interference factors.
Failures occur in the structures of reinforced concrete buildings and facilities during their continuous exploitation, without being overloaded or exposed to extreme impacts, the most common being cracks. Their detection and change in time are related to the assessment of the state of the structures, their safety, and reliability during their construction and especially for their safety exploitation. This paper describes the results of the experimental studies conducted by authors aiming to verify the possibility of using the non-destructive ultrasonic pulse velocity method (NDUPVM) for detection and evaluation of cracks. Results of an experimental study of 12 reinforced concrete beams are presented. In previous experiments, some of them were subjected to bending until the maximum crack width of 0.3 mm was reached and others until yielding of the longitudinal reinforcement. The results obtained from the measurements of the depths of the normal cracks with different widths with NDUPVM were compared with the visually measured ones. In the present research cracks with the same width and with a similar depth were chosen. The influence of extreme external conditions to the accuracy of the measured crack depths by the NDUPVM was investigated. Non-destructive ultrasonic research was done by a portable device Proceq TICO.
The main objective of this paper is to analyze and develop a GIS system that includes all necessary information obtained by using GPS and mobile GIS techniques as well. Since several techniques for information management exist, the aim is how to integrate them for a sustainable management of vineyards areas in Kosovo. It has been designed a system, which is able to produce maps, make various analyses based on the requests of the specific users and offers trend orientation for decision making. The JAVA programming language has been used. This provides the most possible flexibility in the data flow and data management. The structure of the data base is proposed to be designed in that way that the textual and geometric data have been processed in a unique data base in PostGIS PostgreSQL technology.Web GIS technology presented in this paper, shows an advantage comparing to the desktop based technology since it enables an access in real time. Foremost, the application offers an access depending on the roles and privileges for different users. Development of a WEB application for viticulture management will improve the efficiency and decision making process as well.Results show numerous capabilities of GIS methodologies to manage the agricultural crops, in this particular case, the vineyards. Further, the results provide insight into information management in a single system and serve as a basis for similar researches in other areas of agriculture in the future.
José Balsa-Barreiro, Pedro M. Valero-Mora, José L. Berné-Valero
et al.
Naturalistic driving can generate huge datasets with great potential for research. However, to analyze the collected data in naturalistic driving trials is quite complex and difficult, especially if we consider that these studies are commonly conducted by research groups with somewhat limited resources. It is quite common that these studies implement strategies for thinning and/or reducing the data volumes that have been initially collected. Thus, and unfortunately, the great potential of these datasets is significantly constrained to specific situations, events, and contexts. For this, to implement appropriate strategies for the visualization of these data is becoming increasingly necessary, at any scale. Mapping naturalistic driving data with Geographic Information Systems (GIS) allows for a deeper understanding of our driving behavior, achieving a smarter and broader perspective of the whole datasets. GIS mapping allows for many of the existing drawbacks of the traditional methodologies for the analysis of naturalistic driving data to be overcome. In this article, we analyze which are the main assets related to GIS mapping of such data. These assets are dominated by the powerful interface graphics and the great operational capacity of GIS software.
In this paper, we investigate the usage of unmanned aerial vehicles (UAV) to assess the crop geometry with special focus on the crop height extraction. Crop height is classified as a reliable trait in crop phenotyping and recognized as a good indicator for biomass, expected yield, lodging or crop stress. The current industrial standard for crop height measurement is a manual procedure using a ruler, but this method is considered as time consuming, labour intensive and subjective. This study investigates methods for reliable and rapid deriving of the crop height from high spatial, spectral and time resolution UAV data considering the influences of the reference surface and the selected crop height generation method to the final calculation. To do this, we performed UAV missions during two winter wheat growing seasons and generate point clouds from areal images using photogrammetric methods. For the accuracy assessment we compare UAV based crop height with ruler based crop height as current industrial standard and terrestrial laser scanner (TLS) based crop height as a reliable validation method. The high correlation between UAV based and ruler based crop height and especially the correlation with TLS data shows that the UAV based crop height extraction method can provide reliable winter wheat height information in a non-invasive and rapid way. Along with crop height as a single value per area of interest, 3D UAV crop data should provide some additional information like lodging area, which can also be of interest in the plant breeding community.
Stephen W. Cuttler, Jeffrey J. Love, Andrei Swidinsky
Abstract Geomagnetic field data obtained through the INTERMAGNET program are convolved with with magnetotelluric surface impedance from four EarthScope USArray sites to estimate the geoelectric variations throughout the duration of a magnetic storm. A duration of time from June 22, 2016, to June 25, 2016, is considered which encompasses a magnetic storm of moderate size recorded at the Brandon, Manitoba and Fredericksburg, Virginia magnetic observatories over 3 days. Two impedance sites were chosen in each case which represent different responses while being within close geographic proximity and within the same physiographic zone. This study produces estimated time series of the geoelectric field throughout the duration of a magnetic storm, providing an understanding of how the geoelectric field differs across small geographic distances within the same physiographic zone. This study shows that the geoelectric response of two sites within 200 km of one another can differ by up to two orders of magnitude (4484 mV/km at one site and 41 mV/km at another site 125 km away). This study demonstrates that the application of uniform 1-dimensional conductivity models of the subsurface to wide geographic regions is insufficient to predict the geoelectric hazard at a given site. This necessitates that an evaluation of the 3-dimensional conductivity distribution at a given location is necessary to produce a reliable estimation of how the geoelectric field evolves over the course of a magnetic storm.