Map Generation Research Articles

Parking space number detection with multi‐branch convolution attention

AbstractWith the increase of large shopping malls, there are many large parking spaces in complex environments, which increases the difficulty of finding vehicles in such environments. To upgrade the consumer's experience, some car manufacturers have proposed detecting parking space numbers in parking spaces. The detection of parking space number in parking spaces in complex environments has problems such as the diversified background of parking space numbers, tilted direction of parking space numbers, and small parking space number scale. Since no scholar has proposed a high‐performance method for such problems, a parking space number detection model based on the multi‐branch convolutional attention is presented. Firstly, using ResNet50 as the backbone network, a multi‐branch convolutional structure is proposed in the backbone network, which aims to process and fuse the feature map through three parallel branches, and enhance the network to represent ability information by convolutional attention, learn global features to selectively strengthen the features containing helpful information, and improve the ability of the model to detect the parking space number area. Secondly, a high‐level feature enhancement unit is designed to adjust the features channel by channel, obtain more spatial correlation, and reduce the loss of information in the process of feature map generation. The data results of the model on the parking space number dataset CCAG show that the precision, recall, and F‐measure are 84.8%, 84.6%, and 84.7%, respectively, which has certain advantages for parking space number detection.

Boxes-Based Representation and Data Sharing of Road Surface Friction for CAVs

Vehicles can easily lose control unexpectedly when encountering unforeseen hazardous road friction conditions. With automation and connectivity increasingly available to assist drivers, vehicle performance can significantly benefit from a road friction preview map, particularly to identify where and how friction ahead of a vehicle may be suddenly decreasing. Although many techniques enable the vehicle to measure the local friction as driving upon a surface, these encounters limit the ability of a vehicle to slow down before a low-friction surface is already encountered. Using the connectivity of connected and autonomous vehicles (CAVs), a global road friction map can be created by aggregating information from vehicles. A challenge in the creation of these global friction maps is the very large quantity of data involved, and that the measurements populating the map are generated by vehicle trajectories that do not uniformly cover the grid. This paper presents a road friction map generation strategy that aggregates the measured road-tire friction coefficients along the individual trajectories of CAVs into a road surface grid. In addition, through clustering the friction grids further, an insight of this work is that the friction map can be represented compactly by rectangular boxes defined by a pair of corner coordinates in space, a friction value, and a confidence interval within the box. To demonstrate the method, a simulation is presented that integrates traffic simulations, vehicle dynamics and on-vehicle friction estimators, and a highway road surface, where friction is changing in space, particularly over a bridge segment. The experimental results indicate that the road friction distribution can be measured effectively by collecting and aggregating the friction data from CAVs.

Exploring the Environmental Tourism Potentials in Mubi Town, Adamawa State Nigeria

The study aims to assess the environmental tourism potentials of Mubi Town Adamawa state, Nigeria with the view to make recommendations. The study employed both spatial and quantitative data. The spatial data involves the geographical information system Arc GIS 10.3, and remote sensing used for location map generation. The quantitative data was generated from one hundred structured questionnaires administered to the sampled populations. A systematic random sampling technique was employed for the questionnaire administration. This is because it allows all individual members to be chosen for the administration. Observation and photographing were conducted as part of the data collection. Descriptive statistics were utilized for correlating Environmental Tourism Potential (ETP) and Physical Tourism Potential (PTP). The correlation analysis between Environmental Tourism Potentials (ETP) indicates a strong correlation indicated a significant perfect and positive correlation of 0.01 at 2 Tailed. Additionally, the respondents' perceptions regarding the environmental tourism potentials were ranked according to a score point to get the results of the prioritized environmental tourism products. The findings demonstrate that the mountains and the hills give a good scenario as the first in the raking; good and conducive weather conditions as the second. At the same time, the attractive natural landscape is the third. Conclusively, if the potentials are appropriately planned, developed, and managed, it will attract both local and foreign tourists to come to Mubi town. Two recommendations are proposed development plan preparation and preservation and conservation of the identified environmental tourism potentials.

VR technology used to show the practice of architectural node structure

With the development and maturity of virtual reality (VR for short) and artificial intelligence technology, panoramic VR roaming is applied to more and more industries. In the construction engineering industry, due to the complex structure of some key building nodes, the application of panoramic VR roaming technology modeling will play a role in significantly reducing the workload and understanding difficulty of design, construction and management personnel. Therefore, this study improves the parallax map generation method of the belief propagation (BP) algorithm in the panoramic VR roaming to use the form of limit matching and optimizes the energy function and matching primitives in it, so as to propose a spatial model construction method of the panoramic VR roaming based on the improved BP algorithm. The experimental results show that the panoramic VR roaming space model construction method proposed in this study can significantly improve the quality of spatial modeling of building node images, and the normalized values of structural similarity of VR roaming space models designed based on Improved belief propagation (IBP), GC, BP, and DP algorithms are 3.32, 3.23, 2.96, and 2.84, respectively, when the number of iterations is obtained 200 times, is also the highest among the compared methods, for example, when the number of samples is 400, the calculation time of IBP scheme is 14.73 ± 0.85 min, and the highest time of other three schemes is 6.86 ± 0.67 min. Therefore, this virtual space modeling method is designed to be more suitable for application scenarios that require lower computational timeliness.

Implementasi Generate Map dan Pemunculan Objek secara Acak pada Game 3D menggunakan Bahasa C# dan Metode Perlin Noise di Unity: Studi Kasus pada Game Development

<p><span style="text-decoration: underline;"><em>Perlin noise algorithm is used to generate noise on the map surface, creating natural effects in 3D games. In this research, a C# script code has been implemented in Unity that can generate maps and spawn random objects in the game. The research results demonstrate that the implementation of map generation and random object spawning techniques can enhance gameplay quality and provide a more immersive playing experience for users. This research aims to contribute to game development on the Unity platform as well as the development of map This research explores the implementation of procedural content generation (PCG) techniques using C# programming language and Perlin noise method in Unity to generate map and random object spawning in 3D games. The main objective of this research is to develop PCG techniques that can be applied to game development. The generation and random object spawning techniques in 3D games using Perlin noise method.</em></span></p>

RPNet: Rice plant counting after tillering stage based on plant attention and multiple supervision network

Rice is a major food crop and is planted worldwide. Climatic deterioration, population growth, farmland shrinkage, and other factors have necessitated the application of cutting-edge technology to achieve accurate and efficient rice production. In this study, we mainly focus on the precise counting of rice plants in paddy field and design a novel deep learning network, RPNet, consisting of four modules: feature encoder, attention block, initial density map generator, and attention map generator. Additionally, we propose a novel loss function called RPloss. This loss function considers the magnitude relationship between different sub-loss functions and ensures the validity of the designed network. To verify the proposed method, we conducted experiments on our recently presented URC dataset, which is an unmanned aerial vehicle dataset that is quite challenged at counting rice plants. For experimental comparison, we chose some popular or recently proposed counting methods, namely MCNN, CSRNet, SANet, TasselNetV2, and FIDTM. In the experiment, the mean absolute error (MAE), root mean squared error (RMSE), relative MAE (rMAE) and relative RMSE (rRMSE) of the proposed RPNet were 8.3, 11.2, 1.2% and 1.6%, respectively, for the URC dataset. RPNet surpasses state-of-the-art methods in plant counting. To verify the universality of the proposed method, we conducted experiments on the well-know MTC and WED datasets. The final results on these datasets showed that our network achieved the best results compared with excellent previous approaches. The experiments showed that the proposed RPNet can be utilized to count rice plants in paddy fields and replace traditional methods.

Radio map generation approaches for an RSSI-based indoor positioning system

The use of Radio map fingerprinting, which relies on a received signal strength indicator (RSSI), is a popular indoor positioning method that offers high accuracy and cost-effective deployment. However, the generation of an RSSI radio map requires significant time and effort. This paper presents three methods for generating radio maps, with the aim of reducing the time required. The first method involves a dedicated mobile application that collects experimental RSSI data, while the second method uses biharmonic spline interpolation (BSI) to expand a prerecorded experimental radio map. The third method, the Wireless InSite simulator-based method, generates a fully simulated radio map. All three methods are studied in detail to evaluate their effectiveness in reducing the time required for radio map generation. Location estimation is then carried out based on the prerecorded radio maps. The combined method presented in this study increases the efficiency of building indoor positioning mobile applications. Experiments using combined real and simulated datasets collected at An Najah National University and University of Dubai campuses demonstrate that the model outperforms similar methods, improving the localization accuracy to approximately 0.45 meters. This level of accuracy is suitable for a variety of location-based applications, including critical ones such as evacuating people from buildings during emergencies.

Examining effect of super-resolution on AVIRIS-NG data: A precursor to generation of large-scale urban material and natural cover maps

The two-phase Airborne Visible InfraRed Imaging Spectrometer – Next Generation (AVIRIS-NG) campaign over India has generated datasets of numerous natural and artificial surroundings with high signal-to-noise ratios and higher spatial and spectral consistencies. However, the first phase AVIRIS-NG datasets of urban areas have 8.1 m spatial resolution, which is insufficient for producing level-4 urban material and natural cover maps required to model Earth’s processes at the micro-scale or constantly monitor urban processes. The current work addresses this drawback by demonstrating a part of the proposed proof-of-concept approach on the AVIRIS-NG dataset of Ahmedabad, India, wherein super-resolution (SR) is proposed to enhance the hyperspectral image’s spatial resolution and preserve its rich spectral content at the targeted spatial scale before utilizing it for producing detailed urban land cover maps. A step-wise account of the SR process has been provided herein to aid the reader in reproducing these experiments. Super-resolved products generated using four SR algorithms have been compared and validated using a composite framework based on processing time, qualitative visual investigation of selected image patches and spectral profiles, re-interpretation of Wald’s reduced resolution protocol and full-reference image quality metrics. Results suggest the AVIRIS-NG dataset’s successful spectral and spatial fidelity at a larger spatial scale, albeit varying in each super-resolved output. Sparse regression and natural prior (SRP), and anchored neighbourhood regression (ANR) produce the best and poorest super-resolved outputs among the tested SR algorithms. Although gradient profile prior (GPP) generated super-resolved product is the second best, it is also the most computationally expensive algorithm. An additional yardstick in the form of a spectral similarity analysis of randomly selected unknown spectral signatures on super-resolved output against a reference spectral library has been proposed to ascertain spectral content preservation further before utilizing the super-resolved product for urban information extraction. This exercise’s findings reveal the presence of the target urban land cover feature at the unknown pixels with matching scores of greater than 0.5 in both best and worst super-resolved products, thereby advocating the extension of the proposed proof-of-concept to recently launched spaceborne hyperspectral missions’ datasets.

Sinkhole risk mapping and early warning: the case of Camaiore (Italy)

Introduction: Sinkholes are ground collapses that can cause significant damage to infrastructure and buildings. Part of the risk represented by sinkholes is related to their abruptness and the difficulty in spotting in advance their exact location within a sinkhole-prone area. For this reason, urban planning informed by an accurate risk mapping and monitoring is one of the most effective ways to reduce the risk.Methods: In this study, we propose a two-folded procedure based on the examination of ground displacement data measured by a ground-based interferometric radar and on the generation of a sinkhole risk zonation map. We examined 11 years’ worth of ground displacement data measured by a ground-based interferometric radar to search for sinkhole precursors. The analysis was based on averaged displacement time series retrieved from high-coherence pixels scattered around Camaiore, Italy, a test site where a catastrophic sinkhole occurred in 1995. To generate a sinkhole risk map, we evaluated the susceptibility map as derived from a set of predisposing environmental parameters, the vulnerability derived from the thickness of the sedimentary cover that can be linked to the abruptness of the collapse, and the value of the elements at risk from the Italian Real Estate Market Observatory integrated with land cover information for the non-built up areas.Results: The analysis of ground displacement data revealed that Camaiore had not experienced subsidence relatable to incoming sinkholes in the monitored period. However, few cm of vertical movements, which are well correlated with water table oscillations, have been measured and are expected to be of the same order of magnitude of sinkhole precursor deformations. This implies that a phenomenon of the size and velocity of the 1995 event could have likely been detected before its final collapse. The sinkhole risk map identified specific areas that should be closely monitored using in situ and remote sensing instrumentation.Discussion: The sinkhole risk zonation map generated in this study can be used to inform urban planning and risk management strategies. The study also shows the potential of ground-based interferometric radar to detect sinkhole precursors and the importance of integrating different mitigation approaches. Overall, this study can provide insights for sinkhole risk assessment and management in sinkhole-prone areas.

The interaction between enhancer variants and environmental factors as an overlooked aetiological paradigm in human complex disease.

The interactions between genetic and environmental risk factors contribute to the aetiology of complex human diseases. Genome-wide association studies (GWAS) have revealed that most of the genetic variants associated with complex diseases are located in the non-coding part of the genome, preferentially within enhancers. Enhancers are distal cis-regulatory elements composed of clusters of transcription factors binding sites that positively regulate the expression of their target genes. The generation of genome-wide maps for histone marks (e.g., H3K27ac), chromatin accessibility and transcription factor and coactivator (e.g., p300) binding profiles have enabled the identification of enhancers across many human cell types and tissues. Nonetheless, the functional and pathological consequences of the majority of disease-associated genetic variants located within enhancers seem to be rather minor under normal conditions, thus questioning their medical relevance. Here we propose that, due to the prevalence of enhancer redundancy, the pathological effects of many disease-associated non-coding genetic variants might be preferentially (or even only) manifested under environmental stress.

Automated descriptor selection, volcano curve generation, and active site determination using the DescMAP software

The material space for catalyst discovery is expansive. Volcano curves are traditionally employed to provide physical insights into optimal catalyst characteristics for new material selection. Their generation lies on a single descriptor picked using expert knowledge. Here we present DescMAP, a Python-based software, to automate the selection of descriptors, the generation of volcano maps, and the identification of active sites for structure-sensitive reactions. We consider traditional energy-based and geometric descriptors for structure-sensitive reactions. DescMAP is integrated with the Virtual Kinetic Laboratory (VLab) to provide multiple functionalities. It inputs spreadsheets or template files for flexibility and outputs interactive graphs for post-processing. We demonstrate its features using the non-oxidative dehydrogenation of ethane to ethylene over (111) closed-packed surfaces and the methane total oxidation over various Pt facets. It can be easily applied to other complex chemistries and achieves quick screening of potential catalysts. Program summaryProgram title: DescMAPCPC Library link to program files:https://doi.org/10.17632/g399b3xyfy.1Developer's repository link:https://github.com/VlachosGroup/DescriptorMapCode Ocean Capsule:https://codeocean.com/capsule/7598436Licensing provisions: MIT licenseProgramming language: PythonExternal routines: pMuTT, NumPy, Pandas, Matplotlib, Plotly, Scikit-learn, ScipyNature of problem: Screening potential catalysts via microkinetic modeling and generating volcano curves is time-consuming. An automated tool to accelerate this process is lacking.Solution method: Python package with descriptor selection that automates descriptor-based microkinetic modeling. Interactive volcano curves generated for post-analysis.

Cretaceous depositional environment interpretation of offshore Otway Basin cores and wireline logs; application to the generation of basin-scale gross depositional environment maps

Gas production from the Inner Otway Basin commenced in the early 2000s but the deep-water part of this basin remains an exploration frontier. Historically, the understanding of plays in this region were largely model driven and therefore the ground-truthing of depositional environments (DE) and gross depositional environments (GDE) are critical. This aspect has been investigated for the Sherbrook Supersequence (SS) by the integration of legacy wireline and core data, with regional 2D seismic facies mapping of new and reprocessed data from Geoscience Australia’s 2020 Otway Basin seismic program. Core observations were matched to wireline logs and seismic facies with resulting well-based DE interpretations calibrated to seismic resolution Regional GDE intervals. Integration of well and seismic observations led to the compilation of a basin-wide Regional GDE map for the Sherbrook SS. This GDE map indicates the distribution of Sherbrook SS play elements such as source rock, seal and reservoir, especially across the Deep-water Otway Basin where well data is sparse.

Multi-directional broad learning system for the unsupervised stereo matching method

The supervised stereo matching methods usually rely on ground truth disparity maps as the training labels, this limits its practical application in many situations. In this study, we propose a novel unsupervised stereo matching method based on a multi-directional broad learning system. A multi-directional broad learning system was constructed to generate multiple candidate disparity maps. During the generation of each candidate disparity map, an update criterion is proposed for the disparity value based on the maximum similarity of the inverse mapping region to remove the abnormal disparity values of the training samples. Subsequently, multi-direction consistency verification is performed to further eliminate abnormal disparity values, which are based on the uniqueness principle of disparity truth values at the same location. Finally, an invalid depth redefinition based on a local gravity weight method is introduced to select the appropriate disparity value to fill the invalid pixel positions from their neighborhood, which is calculated based on the local region of the color, matching cost, and geometric spaces in the stereo images. We provide the results of experiments on both indoor and outdoor scenarios to demonstrate the effectiveness and flexibility of our approach, including comparisons with state-of-the-art methods.

A system for the validation of collision avoidance algorithm performance of autonomous ships

Adequate validations on intelligent ships are essential to guarantee navigation safety and efficiency. In this paper an emulated validation system is constructed to test collision avoidance ability of autonomous ships in open waters, coastal regions and archipelago areas with different traffic densities. A synthetic map generator is designed, the map meshing conbines the techniques from Voronoi Tessellation, Poisson-disk Sampling and Perlin Noise to build up more natural coastlines and detailed elevation data. To imitate random encounter scenarios, a new method is designed to maintain target ship quantity, to conduct various tests, 3 types of water geography, different ship behaviors as well as manual and automatic control modes are applied, during testing, each ship is designed to follow the planned routes which are generated arbitrarily and then smoothed. A validation scheme is put forward for comprehensive testing procedures. Finally, A set of experiments on the collision avoidance algorithm show the merits and drawbacks, this verifies the effectiveness of the validation system.

Remote Laboratory Experiments: Fan Map Generation and Airfoil Flow Characterization

This paper presents the procedure followed to conduct two various experiments as part of the ‘Mechanical Laboratory’ course of the Mechanical Engineering department, at Aristotle University of Thessaloniki, during the Covid-19 period. Adapting to the personal contact restrictions imposed by the health issue outbreak, a remote experimental method is proposed. In the first experiment, the students had to generate the operational map of a cooling computer fan while on the second one the characteristic parameters of a symmetric airfoil needed to be measured. Multifunctional control panel interfaces were introduced to the students. Through these applications, the student had the opportunity to run the experiments, assess the raw data, monitor all the necessary flow parameters and acquire the important variables. The students had to post-process the results and submit two reports, one for each experiment. This teaching method provided the students with the experience of real research project and guided them to use modern tools to enhance the quality of their future work. This publication focuses on the address of the challenges, arose during the automation of the experiments. The remote experimental sessions indicated some advantages in learning procedure which was also reflected on the assignment grades. The development of the procedure has been successfully implemented and tested over a couple of years while the quarantine measure have been in place. Furthermore, the experience gained will be used to implement modern communication protocols in the experimental set-ups.

Performance Verification of Integrated Module for Automatic Analysis of Digital Maps in Earthwork Sites

Research on digitalization of construction sites using UAV (Unmanned Aerial Vehicles), UGV (Unmanned Ground Vehicles), and three-dimensional scanners is rapidly progressing. However, most 3D digital map generation and analysis technologies are still performed individually by utilizing libraries and software that require human intervention. In this study, we developed a digital map automatic analysis framework by reviewing five algorithms required for digital map generation and analysis for 3D digital map generation and analysis automation, defining the optimal forward and backward relationship between algorithms. In addition, based on the developed framework, the "digital map automatic analysis integrated module" is developed by considering the scan data format conversion and data processing process for automation. Furthermore, the effectiveness of the development module was verified by automating and speed testing the developed "digital map automatic analysis integrated module".

Simulation of BLDC Motor Drive Systems for Electric Vehicles Using Matlab Simulink

The defining scientific developments of our time would not have been possible without the use of simulations. The aim of the research is to create a simulation of a BLDC motor. When creating a simulation, great emphasis must be placed on defining the purpose of the simulation. This basically determines the structure and complexity of the model. The model discussed here was created so that an optimization task could be defined more precisely by inserting it as a sub-model into a vehicle dynamics model. Scalability was another aspect, that is, to be able to increase the accuracy of the model with measured data in the future, as well as to be able to validate it. During the research, a BLDC motor efficiency map generation program was created, as well as an environment for testing the generated data. The created system gives researchers the opportunity to use a shape-correct efficiency model when simulating a BLDC motor even without measured data. This makes it possible to discover real relationships between model parameters when performing optimization.

An edge guided coarse-to-fine generative network for image outpainting

Deep-learning based generative models have achieved outstanding performance in various image processing tasks. This paper introduces a method to address the problem of extrapolating or outpainting visual context. When the input size is a small proportion of the output size, a limited amount of information is present to regenerate a semantically coherent image. This task is challenging because the missing region of the original image may include crucial semantic and spatial structural information, which is difficult to predict from the input. We propose a three-stage edge-guided coarse-to-fine generative network model, consisting of a contextual inference network, structural edge map generator and edge enhanced network, to synthesise semantically consistent output from small picture inputs. Our model adopts a gradual growth inference strategy in the contextual inference network so that the generated image can present a more coherent structure, and this result can support the structural edge map generator to generate a reasonable edge map in a large missing area. Combining the contextual inference network and structural edge map generator outputs enables the edge enhanced network to generate more convincing images. We evaluate our model using four public datasets: CelebA, Places2, Oxford Flower102and CUB200. Our experimental results demonstrate that the proposed image outpainting network can successfully regenerate high-quality images with a large missing region even when some structural features are lost in the input images.

Using morphometric analysis for assessment of flash flood susceptibility in the Mediterranean region of Turkey

Flash floods are one of the most severe natural disasters around the world because of their rapid and unpredictable nature. It is expected that the frequency and intensity of flood events will increase because of extreme rainfall events induced by climate change. In this context, the generation of a flood susceptibility map contributes to effective flood management in a basin. The present study aims to generate a flash flood susceptibility map for the Imali Stream Basin (ISB) situated within the Mediterranean region of Turkey. For this purpose, morphometric analysis, geographic information system (GIS), remote sensing (RS), and principal component analysis (PCA) were used in this study. ASTER GDEM (v.3) was used to delineate 9 sub-watersheds and to obtain the required morphometric parameters. To generate a flash flood susceptibility map, the original compound values ​​calculated for each sub-watershed were transformed into values ​​between 0 and 1 by using the min-max normalization method. Then, these values were divided into 3 classes called low, moderate, and high by using the equal interval classification method in ArcGIS. According to both flash flood susceptibility maps produced by using morphometric analysis and PCA, sub-watershed 5 has the highest flash flood susceptibility in the basin. The flash flood that occurred in sub-watershed 5 in 2016 contributes to these results. Therefore, flood management based on morphometric analysis can be a highly effective method for decision-makers and planners in the Mediterranean region, one of the hotspots to be affected by climate change.

A multi‐stage model for bird's eye view prediction based on stereo‐matching model and RGB‐D semantic segmentation

AbstractBird's‐Eye‐View (BEV) map is a powerful and detailed scene representation for intelligent vehicles that provides both the location and semantic information about nearby objects from a top‐down perspective. BEV map generation is a complex multi‐stage task, and the existing methods typically perform poorly for distant scenes. Thus, the authors introduce a novel multi‐stage model that infers to obtain more accurate BEV map. First, the authors propose the Adaptive Aggregation with Stereo Mixture Density (AA‐SMD) model, which is an improved stereo matching model that eliminates bleeding artefacts and provides more accurate depth estimation. Next, the authors employ the RGB‐Depth (RGB‐D) semantic segmentation model to improve the semantic segmentation performance and connectivity of their model. The depth map and semantic segmentation maps are then combined to create an incomplete BEV map. Finally, the authors propose a Multi Strip Pooling Unet (MSP‐Unet) model with a hierarchical multi‐scale (HMS) attention and strip pooling (SP) module to improve prediction with BEV generation. The authors evaluate their model with a Car Learn to Act (CARLA)‐generated synthetic dataset. The experiment results demonstrate that the authors’ model generates a highly accurate representation of the surrounding environment achieving a state‐of‐the‐art result of 61.50% Mean Intersection‐over‐Union (MIoU) across eight classes.