Traditional Map Research Articles

Three-Dimensional Reconstruction of Zebra Crossings in Vehicle-Mounted LiDAR Point Clouds

In the production of high-definition maps, it is necessary to achieve the three-dimensional instantiation of road furniture that is difficult to depict on traditional maps. The development of mobile laser measurement technology provides a new means for acquiring road furniture data. To address the issue of traffic marking extraction accuracy in practical production, which is affected by degradation, occlusion, and non-standard variations, this paper proposes a 3D reconstruction method based on energy functions and template matching, using zebra crossings in vehicle-mounted LiDAR point clouds as an example. First, regions of interest (RoIs) containing zebra crossings are obtained through manual selection. Candidate point sets are then obtained at fixed distances, and their neighborhood intensity features are calculated to determine the number of zebra stripes using non-maximum suppression. Next, the slice intensity feature of each zebra stripe is calculated, followed by outlier filtering to determine the optimized length. Finally, a matching template is selected, and an energy function composed of the average intensity of the point cloud within the template, the intensity information entropy, and the intensity gradient at the template boundary is constructed. The 3D reconstruction result is obtained by solving the energy function, performing mode statistics, and normalization. This method enables the complete 3D reconstruction of zebra stripes within the RoI, maintaining an average planar corner accuracy within 0.05 m and an elevation accuracy within 0.02 m. The matching and reconstruction time does not exceed 1 s, and it has been applied in practical production.

Construction of Metaphorical Maps of Cyberspace Resources Based on Point‐Cluster Feature Generalization

ABSTRACTIn the digital age, the expansion of cyberspace has resulted in increasing complexity, making clear cyberspace visualization crucial for effective analysis and decision‐making. Current cyberspace visualizations are overly complex and fail to accurately reflect node importance. To address the challenge of complex cyberspace visualization, this study introduces the integrated centrality metric (ICM) for constructing a metaphorical map that accurately reflects node importance. The ICM, a novel node centrality measure, demonstrates superior accuracy in identifying key nodes compared to degree centrality (DC), k‐shell centrality (KC), and PageRank values. Through community partitioning and point‐cluster feature generalization, we extract a network's hierarchical structure to intuitively represent its community and backbone topology, and we construct a metaphorical map that offers a clear visualization of cyberspace. Experiments were conducted on four original networks and their extracted backbone networks to identify core nodes. The Jaccard coefficient was calculated considering the results of the three aforementioned centrality measures, ICM, and the SIR model. The results indicate that ICM achieved the best performance in both the original networks and all extracted backbone networks. This demonstrates that ICM can more precisely evaluate node importance, thereby facilitating the construction of metaphorical maps. Moreover, the proposed metaphorical map is more convenient than traditional topological maps for quickly comprehending the complex characteristics of networks.

Chaotic dynamics of a delayed fractional order MLNCML system

Abstract We present a delayed fractional order spatiotemporal dynamical system using the mixed linear-nonlinear coupling connections between lattices. In the simulation experiments, the dynamical characteristics of the proposed model are looked into and analyzed by using bifurcation diagrams and metric entropy density. The proposed model has fewer periodic windows in its bifurcation diagrams, large key space and strong ergodicity due to the delay. These excellent properties are of great significance in encryption. Moreover, the parameter μ breaks its fixed range in the traditional logistic map, which can contribute to enlarge the key space of cryptosystem. Through numerical simulations and security analysis, it is found that the proposed model is applied for encryption.

Digital 3D Hologram Generation Using Spatial and Elevation Information

The evolution of cartography poses challenges in representing three-dimensional terrain accurately on traditional two-dimensional maps. Providing an accurate 3D view of the area, coupled with essential geographic information, is vital for rapid and accurate decision-making in emergency management and response. Holography offers a promising solution by providing immersive three-dimensional visualizations. The field of hologram mapping, although novel, is still developing. Given its nascent stage, several limitations are evident. This study addresses one such limitation—inaccuracies in distance measurement—by presenting a hologram map that integrates two-dimensional and three-dimensional information. Accurate distance information on maps is critical for operational success. We aimed to improve hologram maps by integrating contour lines. Our approach allows users to measure distances from near-perpendicular angles while viewing 3D features from other perspectives. We review current advancements in hologram mapping, highlight existing limitations, and introduce our innovative solution designed to enhance both accuracy and usability. Our experiment resulted in a hologram map that accurately depicts a 3D environment, integrates contour lines, and allows for distance and slope angle measurements. The hologram map fills the research gap by providing accurate 3D visualization and distance measurement, signifying a major advancement in hologram mapping.

Image encryption algorithm based on a novel cascade chaotic system and DNA mutation

The image is an important medium for information transfer. To ensure image security and improve encryption efficiency, a novel image encryption algorithm is proposed in this paper. Firstly, a new chaotic map is proposed by combining traditional chaotic maps. The SHA-256 algorithm processes the input image to produce chaotic parameters that generate random sequences associated with the plaintext. Secondly, the plain image is encoded using a fast-dynamic DNA encoding method to obtain the DNA matrix. Thirdly, a new DNA sequence operation method called DNA frame shift mutation is designed. The DNA matrix after performing the frame shift mutation transformation is decoded into a pixel matrix. Finally, coordinate matrices are constructed and the pixel positions are randomly selected for dynamic diffusion, which enables simultaneous scrambling and diffusion at the pixel level. The values of the information entropy are all greater than or equal to 7.9993. Experimental results show that the proposed algorithm is secure and it can be resistant to various attacks. Meanwhile, it has sound encryption efficiency.

The reliability and validity of rapid transcranial magnetic stimulation mapping for muscles under active contraction

Rapid mapping is a transcranial magnetic stimulation (TMS) mapping method which can significantly reduce data collection time compared to traditional approaches. However, its validity and reliability has only been established for upper-limb muscles during resting-state activity. Here, we determined the validity and reliability of rapid mapping for non-upper limb muscles that require active contraction during TMS: the masseter and quadriceps muscles. Eleven healthy participants attended two sessions, spaced two hours apart, each involving rapid and ‘traditional’ mapping of the masseter muscle and three quadriceps muscles (rectus femoris, vastus medialis, vastus lateralis). Map parameters included map volume, map area and centre of gravity (CoG) in the medial-lateral and anterior-posterior directions. Low to moderate measurement errors (%SEMeas = 10–32) were observed across muscles. Relative reliability varied from good-to-excellent (ICC = 0.63–0.99) for map volume, poor-to-excellent (ICC = 0.11–0.86) for map area, and fair-to-excellent for CoG (ICC = 0.25–0.8) across muscles. There was Bayesian evidence of equivalence (BF’s > 3) in most map outcomes between rapid and traditional maps across all muscles, supporting the validity of the rapid mapping method. Overall, rapid TMS mapping produced similar estimates of map parameters to the traditional method, however the reliability results were mixed. As mapping of non-upper limb muscles is relatively challenging, rapid mapping is a promising substitute for traditional mapping, however further work is required to refine this method.

Refined Landslide Susceptibility Mapping Considering Land Use Changes and InSAR Deformation: A Case Study of Yulin City, Guangxi

Landslide susceptibility maps (LSMs) are valuable tools typically used by local authorities for land use management and planning activities, supporting decision-makers in urban and infrastructure planning. To address this, we proposed a refined method for landslide susceptibility assessment, which comprehensively considered both static and dynamic factors. Neural network methods were used for susceptibility analysis. Land use and land cover (LULC) change and InSAR deformation were then integrated into the traditional susceptibility zoning to obtain a refined susceptibility map with higher accuracy. Validation was conducted on the improved landslide susceptibility map using site landslide data. The results showed that the LULC were proven to be the core driving factors for landslide occurrence in the study area. The GRU model achieved the highest model performance (AUC = 0.886). The introduction of InSAR surface deformation and land use and land cover change data could rationalize the inappropriateness of traditional landslide susceptibility zoning, correcting the false positive and false negative areas in the traditional landslide susceptibility map caused by human activities. Ultimately, 12.25% of the study area was in high-susceptibility zones, with 3.10% of false positive and 0.74% of false negative areas being corrected. The proposed method enabled refined analysis of landslide susceptibility over large areas, providing technical support and disaster prevention and mitigation references for geological hazard susceptibility assessment and land management planning.

Optimized Random Forest Method for 3D Evaluation of Coalbed Methane Content Using Geophysical Logging Data.

Accurate evaluation of coalbed methane (CBM) content is crucial for effective exploration and development. Traditional gas content measurement methods based on laboratory analysis of drill core samples are costly, whereas geophysical logging methods offer a cost-effective alternative by providing continuous high-resolution profiles of rock layer physical properties. However, the relationship between CBM content and geophysical logging data is complex and nonlinear, necessitating an advanced prediction method. This study focuses on the No. 3 coal seam in the Shizhuang South Block of the Qinshui Basin, utilizing geophysical logging data and 148 sets of laboratory core samples. We employed the Random Forest (RF) method optimized with a simulated annealing-genetic algorithm (SA-GA) to develop the SA-GA-RF model for evaluating CBM content. The model's performance was validated using test data and new CBM well data, and it was applied to calculate the vertical gas content profiles of No. 3 coal seam across 128 wells. The SA-GA-RF model demonstrated an average relative error of 13.13% in the test data set, outperforming Backpropagation Neural Network (BPNN), Least Squares Support Vector Machine (LSSVM), Extreme Learning Machine (ELM), and multivariate regression (MR) methods. The model also exhibited strong generalizability in new wells and improved model-building efficiency compared to traditional cross-validation grid search methods. The construction of a three-dimensional CBM content model, incorporating well coordinates and elevation data, allowed for detailed identification of high gas content areas and layers. This three-dimensional model offers a more precise characterization than traditional two-dimensional isopleth maps, providing valuable insights for CBM exploration, reserve evaluation, and production optimization.

Soil loss estimation using RUSLE model: Comparison of conventional and digital soil data at watershed scale in central Iran

Sustainable agriculture and eco-environment conservation face constant threats from soil erosion in mountainous regions. This study aimed to predict soil loss in the mountainous watershed by applying the Revise Universal Equation Soil Loss (RUSLE) and Sediment Delivery Ratio (SDR) models and to evaluate the efficacy of the RUSLE model through the observed data in a hydrometric station. Comparing a polygonal, and a digital soil map (prepared by the Kriging method) for deriving the K factor to estimate soil loss was another objective of the recent research. In this study, two scenarios were examined for the calculation of spatial variation of the K-factor; scenario I comprised soil data derived from a legacy soil map with eight soil units and the data from their representative soil profiles (i.e., traditional soil survey), and scenario II comprised K-factor derived from 100 studied sites using kriging technique (i.e., digital soil survey). The results of RUSLE modeling showed that there was no significant difference between scenario I (7.97 t/(ha. yr)) and scenario II (7.93 t/(ha. yr)) for predicting soil loss with the RUSLE model. This finding confirms that the RUSLE model with limited and legacy data can provide a reliable prediction of soil loss in the given watershed. Moreover, long and short-term periods were used to estimate soil loss, while in the long-term period, estimated soil loss had higher accordance with actual soil loss. Sediment delivery ratio was calculated using models of Vanoni, Boyce, USDA-SCS, and Slope-based models. The results indicated that the Slope-based model had the highest fitness (R2=0.946, ME=0.27, and RMSE=0.275 for scenario I and R2=0.9443, ME=0.26 and RMSE=0.40 for scenario II), with observed sedimentation rate at the hydrometric station at the outlet of the watershed. Overall, predicted soil loss severity map using the traditional soil map by RUSLE model could provide trustworthy information for decision makers and governors at the given and similar watersheds in semiarid regions.

Quantitative transport mapping of multi-delay arterial spin labeling MRI detects early blood perfusion alterations in Alzheimer’s disease

BackgroundQuantitative transport mapping (QTM) of blood velocity, based on the transport equation has been demonstrated higher accuracy and sensitivity of perfusion quantification than the traditional Kety’s method-based cerebral blood flow (CBF). This study aimed to investigate the associations between QTM velocity and cognitive function in Alzheimer’s disease (AD) using multiple post-labeling delay arterial spin labeling (ASL) MRI.MethodsA total of 128 subjects (21 normal controls (NC), 80 patients with mild cognitive impairment (MCI), and 27 AD) were recruited prospectively. All participants underwent MRI examination and neuropsychological evaluation. QTM velocity and traditional CBF maps were computed from multiple delay ASL. Regional quantitative perfusion measurements were performed and compared to study group differences. We tested the hypothesis that cognition declines with reduced cerebral blood perfusion with consideration of age and gender effects.ResultsIn cortical gray matter (GM) and the hippocampus, QTM velocity and CBF showed decreased values in the AD group compared to NC and MCI groups; QTM velocity, but not CBF, showed a significant difference between MCI and NC groups. QTM velocity and CBF showed values decreasing with age; QTM velocity, but not CBF, showed a significant gender difference between male and female. QTM velocity and CBF in the hippocampus were positively correlated with cognition, including global cognition, memory, executive function, and language function.ConclusionThis study demonstrated an increased sensitivity of QTM velocity as compared with the traditional Kety’s method-based CBF. Specifically, we observed only in QTM velocity, reduced perfusion velocity in GM and the hippocampus in MCI compared with NC. Both QTM velocity and CBF demonstrated a reduction in AD vs. controls. Decreased QTM velocity and CBF in the hippocampus were correlated with poor cognitive measures. These findings suggest QTM velocity as potential biomarker for early AD blood perfusion alterations and it could provide an avenue for early intervention of AD.

Efficient Parallel Processing of R-Tree on GPUs

R-tree is an important multi-dimensional data structure widely employed in many applications for storing and querying spatial data. As GPUs emerge as powerful computing hardware platforms, a GPU-based parallel R-tree becomes the key to efficiently port R-tree-related applications to GPUs. However, traditional tree-based data structures can hardly be directly ported to GPUs, and it is also a great challenge to develop highly efficient parallel tree-based data structures on GPUs. The difficulty mostly lies in the design of tree-based data structures and related operations in the context of many-core architecture that can facilitate parallel processing. We summarize our contributions as follows: (i) design a GPU-friendly data structure to store spatial data; (ii) present two parallel R-tree construction algorithms and one parallel R-tree query algorithm that can take the hardware characteristics of GPUs into consideration; and (iii) port the vector map overlay system from CPU to GPU to demonstrate the feasibility of parallel R-tree. Experimental results show that our parallel R-tree on GPU is efficient and practical. Compared with the traditional CPU-based sequential vector map overlay system, our vector map overlay system based on parallel R-tree can achieve nearly 10-fold speedup.

Self-Calibration for Star Sensors.

Aiming to address the chicken-and-egg problem in star identification and the intrinsic parameter determination processes of on-orbit star sensors, this study proposes an on-orbit self-calibration method for star sensors that does not depend on star identification. First, the self-calibration equations of a star sensor are derived based on the invariance of the interstar angle of a star pair between image frames, without any requirements for the true value of the interstar angle of the star pair. Then, a constant constraint of the optical path from the star spot to the center of the star sensor optical system is defined to reduce the biased estimation in self-calibration. Finally, a scaled nonlinear least square method is developed to solve the self-calibration equations, thus accelerating iteration convergence. Our simulation and analysis results show that the bias of the focal length estimation in on-orbit self-calibration with a constraint is two orders of magnitude smaller than that in on-orbit self-calibration without a constraint. In addition, it is shown that convergence can be achieved in 10 iterations when the scaled nonlinear least square method is used to solve the self-calibration equations. The calibrated intrinsic parameters obtained by the proposed method can be directly used in traditional star map identification methods.

Detecting Road Intersections from Crowdsourced Trajectory Data Based on Improved YOLOv5 Model

In recent years, the rapid development of autonomous driving and intelligent driver assistance has brought about urgent demands on high-precision road maps. However, traditional road map production methods mainly rely on professional survey technologies, such as remote sensing and mobile mapping, which suffer from high costs, object occlusions, and long updating cycles. In the era of ubiquitous mapping, crowdsourced trajectory data offer a new and low-cost data resource for the production and updating of high-precision road maps. Meanwhile, as key nodes in the transportation network, maintaining the currency and integrity of road intersection data is the primary task in enhancing map updates. In this paper, we propose a novel approach for detecting road intersections based on crowdsourced trajectory data by introducing an attention mechanism and modifying the loss function in the YOLOv5 model. The proposed method encompasses two key steps of training data preparation and improved YOLOv5s model construction. Multi-scale training processing is first adopted to prepare a rich and diverse sample dataset, including various kinds and different sizes of road intersections. Particularly to enhance the model’s detection performance, we inserted convolutional attention mechanism modules into the original YOLOv5 and integrated other alternative confidence loss functions and localization loss functions. The experimental results demonstrate that the improved YOLOv5 model achieves detection accuracy, precision, and recall rates as high as 97.46%, 99.57%, and 97.87%, respectively, outperforming other object detection models.

A 5K Efficient Low-Light Enhancement Model by Estimating Increment between Dark Image and Transmission Map Based on Local Maximum Color Value Prior

Low-light enhancement (LLE) has seen significant advancements over decades, leading to substantial improvements in image quality that even surpass ground truth. However, these advancements have come with a downside as the models grew in size and complexity, losing their lightweight and real-time capabilities crucial for applications like surveillance, autonomous driving, smartphones, and unmanned aerial vehicles (UAVs). To address this challenge, we propose an exceptionally lightweight model with just around 5K parameters, which is capable of delivering high-quality LLE results. Our method focuses on estimating the incremental changes from dark images to transmission maps based on the low maximum color value prior, and we introduce a novel three-channel transmission map to capture more details and information compared to the traditional one-channel transmission map. This innovative design allows for more effective matching of incremental estimation results, enabling distinct transmission adjustments to be applied to the R, G, and B channels of the image. This streamlined approach ensures that our model remains lightweight, making it suitable for deployment on low-performance devices without compromising real-time performance. Our experiments confirm the effectiveness of our model, achieving high-quality LLE comparable to the IAT (local) model. Impressively, our model achieves this level of performance while utilizing only 0.512 GFLOPs and 4.7K parameters, representing just 39.1% of the GFLOPs and 23.5% of the parameters used by the IAT (local) model.

Enhancing Wave Function Collapse Algorithm for Procedural Map Generation Problem

In this study, the Improved Map Generation Algorithm (GHAO) method is presented to improve traditional methods in procedural map creation. Traditional procedural map generation techniques using noise generation exhibit shortcomings in the consistent composition of a real map with its uniformly distributed features. On the other hand, procedural map creation techniques that use wave function collapse require that some map pieces already exist to create a map. The observed disadvantages were eliminated by using a hybrid technique with the designed GHAO method. The developed algorithm is similar to real maps in terms of the distribution of map regions, does not need 3D model parts, and performs map creation operations without increasing the algorithm's time complexity. The evaluation of IMGA was carried out by coding the method into the Unity game engine.

An Automated Chemical Exploration of NGC 6334I at 340 au Resolution

Much of the information gleaned from observations of star-forming regions comes from the analysis of their molecular emission spectra, particularly in the radio regime. The time-consuming nature of fitting synthetic spectra to observations interactively for such line-rich sources, however, often results in such analysis being limited to data extracted from a single-dish observation or a handful of pixels from an interferometric observation. Yet, star-forming regions display a wide variety of physical conditions that are difficult, if not impossible, to accurately characterize with such a limited number of spectra. We have developed an automated fitting routine that visits every pixel in the field of view of an Atacama Large Millimeter/submillimeter Array (ALMA) data cube and determines the best-fit physical parameters, including excitation temperature and column densities, for a given list of molecules. In this proof-of-concept work, we provide an overview of the fitting routine and apply it to 0.″26, 1.1 km s−1 resolution ALMA observations of two sites of massive star formation in NGC 6334I. Parameters were found for 21 distinct molecules by generating synthetic spectra across 7.48 GHz of spectral bandwidth between 280 and 351 GHz. Spatial images of the derived parameters for each of the >8000 pixels are presented with special attention paid to the C2H4O2 isomers and their relative variations. We highlight the greater scientific utility of the column density and velocity images of individual molecules compared to traditional moment maps of single transitions.

Szanse reintegracji a remediacja cyfrowa geografii

Nowadays, geographers and specialists in other disciplines perceive information and communication technologies, including GIS software, as part of the inevitable changes in practice and science (including geography). Recent ways of generating, processing, storing, analyzing and sharing data, creating and sharing texts, visualizing, mapping, analyzing, communicating ideas, video, podcasts, and presentations, sharing information, social networks and mainstream media are entirely dependent on computing technologies. Modern science in the digital age uses big data, artificial intelligence – machine learning methods (data mining, pattern recognition, geovisualization, spatial statistics, optimization and spatial simulation), which in turn have renewed interest in and ties with the fields of computational social sciences and data-driven geography, to gain deeper insights into quantitative, time-scale, multi-resolution and multi-scale research. The tool and product of a geographer’s research is, among other things, the map, a medium, a vehicle for geographic theories and ideas and a tool for conveying geo-information. However, the remediation of a traditional map into a digital map involves the transformation, addition, restriction or change of the meaning system of the original. The new digital medium arouses different attitudes, different emotions and triggers new practices of receiving geo-information among professionals and the public. Undoubtedly, this also applies to researchers–geographers who apply in their geospatial research multi-, inter- or transdisciplinary approaches based on different research paradigms. Integrated and problem-focused scientific discourse in geographic research and digital remediation of maps allow us to look at the future of (currently diversified) geographic research with some hope for its reintegration. The aim of the paper is to identify the possibilities, current and prospective paths and research topics of geography in Poland, which are interdisciplinary, combining the field of interest of physical, socio-economic geographers and specialists of space economy, using the modern instrumentarium of information and communication technologies in geography, i.e. geographic information systems.

A Novel Method for Regional Prospecting Based on Modern 3D Graphics

During comprehensive regional prospecting evaluation and delineation of a prospecting target area, various types of data, including geological, geophysical, geochemical, and remote sensing, are usually integrated and visualized in a unified spatial environment, making it convenient for researchers to identify mineralization. To maximize the precision of spatial boundaries, the maps traditionally used in prospecting are predominantly in vector formats. However, with the rapid development of modern real-time 3D graphics and computer cartography technology, raster maps can now provide richer detail representation compared to traditional vector maps while still meeting the precision requirements. In this paper, we present a new GPU-based 3D visualization method for spatial data, specifically, two types of bitmap-based maps called dynamic geochemical maps (DGMs) and interactive geological maps (IGMs). A novel software system implementing this method was developed and has been applied in the exploration of the Zhunuo ore district, Tibet, showing large advantages over traditional vector maps.

Dynamic map‐making technology and the application in geographic information science

AbstractThe dynamic map is applied instead of traditional static maps in the teaching of geographic information science (GIS)‐related majors so as to meet the need for teaching animations and teaching interaction in new teaching methods such as mobile personalized learning, massive open online course, and Flipped Classroom. The research introduces the concept and the making tools of dynamic maps, studies the making principles and key technologies of dynamic maps, and constructs the relevant theories such as the “frame” model based on time granularity, the dynamic interpolation algorithm based on spatial characteristics, dynamic symbolic logic model, and algorithm. Based on the above principles and theories, taking a dynamic map of the change in the population of each city in Shandong province as an example, the research demonstrates the making method and process of the dynamic maps for teaching with Flash MX. At last, it analyzes the role of dynamic map in GIS‐related courses teaching and evaluates the teaching effect of dynamic maps.

A comprehensive research of deep learning approaches for High Definition map construction in autonomous driving

In the realm of autonomous driving, High Definition Maps (HD maps) are indispensable for safe and precise navigation. Traditional HD map construction methods involving point cloud capture and SLAM have proven effective but labor-intensive. This paper addresses the growing interest in leveraging deep learning techniques to streamline HD map creation. This paper presents a systematic exploration of deep learning methodologies for HD map construction. It categorizes these approaches into two core components: Feature Extraction and Feature Decoding. Feature Extraction involves the transformation of input data, comprising images and LiDAR point clouds, into Bird's Eye View (BEV) representations. Feature Decoding is dissected into rasterized map objectives and vector map objectives. Detailed analysis is conducted on prominent methodologies. The paper provides a nuanced evaluation of these deep learning techniques, highlighting their respective strengths and limitations. Factors such as precision, computational efficiency, and the preservation of fine-grained details are considered when selecting the most suitable method. This comprehensive review summarizes and prospects the research in related fields.