scholarly journals Deep Graph Convolutional Networks for Accurate Automatic Road Network Selection

2021 ◽  
Vol 10 (11) ◽  
pp. 768
Author(s):  
Jing Zheng ◽  
Ziren Gao ◽  
Jingsong Ma ◽  
Jie Shen ◽  
Kang Zhang
Keyword(s):  
Road Network ◽  
Spatial Information ◽  
Road Networks ◽  
Selection Effect ◽  
Network Selection ◽  
Small Scale ◽  
Attention Networks ◽  
Convolutional Networks ◽  
Spatial Features ◽  
Selection Of

The selection of road networks is very important for cartographic generalization. Traditional artificial intelligence methods have improved selection efficiency but cannot fully extract the spatial features of road networks. However, current selection methods, which are based on the theory of graphs or strokes, have low automaticity and are highly subjective. Graph convolutional networks (GCNs) combine graph theory with neural networks; thus, they can not only extract spatial information but also realize automatic selection. Therefore, in this study, we adopted GCNs for automatic road network selection and transformed the process into one of node classification. In addition, to solve the problem of gradient vanishing in GCNs, we compared and analyzed the results of various GCNs (GraphSAGE and graph attention networks [GAT]) by selecting small-scale road networks under different deep architectures (JK-Nets, ResNet, and DenseNet). Our results indicate that GAT provides better selection of road networks than other models. Additionally, the three abovementioned deep architectures can effectively improve the selection effect of models; JK-Nets demonstrated more improvement with higher accuracy (88.12%) than other methods. Thus, our study shows that GCN is an appropriate tool for road network selection; its application in cartography must be further explored.

scholarly journals Zero Traffic Conflict (ZTC) Road Networks with Concentric Traffic Paths

2021 ◽  
Author(s):  
Richard Tasgal ◽  
David Eichler
Keyword(s):  
Road Network ◽  
Road Networks ◽  
Vehicular Traffic ◽  
Careful Selection ◽  
Traffic Loads ◽  
Traffic Conflict ◽  
Left Turns ◽  
Travel Distances ◽  
Selection Of

U-turns and left turns are sometimes forbidden even though it increases travel distances. The greater travel distances are sometimes outweighed by the improved movement through intersections due to there being fewer conflicting lanes of traffic. One can, further, forbid straight-throughs. Restricting a sufficient number of turns can make intersections free from crossing lanes of traffic (``zero traffic conflict,'' ``ZTC"), though there may still be merging lanes of traffic. It's possible to make \begin{it}all\end{it} intersections in a road \begin{it}network\end{it} ZTC. However, keeping all destinations accessible and travel distances moderate requires careful selection of allowed driving directions and turning directions. We demonstrate through numerical microscopic and macroscopic simulations that there are road networks and ranges of traffic loads for which, in comparison with conventional schemes, ZTC road network can carry approximately $50$\% more vehicular traffic without incurring gridlock.

scholarly journals Evaluating map specifications for automated generalization of settlements and road networks in small-scale maps

2019 ◽  
Vol 23 (4) ◽  
pp. 242-255
Author(s):  
Karolina Maja Sielicka ◽  
Izabela Karsznia
Keyword(s):  
Knowledge Base ◽  
Road Network ◽  
Input Data ◽  
Selection Process ◽  
Road Networks ◽  
Test Area ◽  
Small Scale ◽  
Geographic Object ◽  
Selection Operators ◽  
Selection Methodology

Abstract The presented research concerns the methodology for selecting settlements and road networks from 1:250 000 to 1:500 000 and 1:1 000 000 scales. The developed methodology is based on the provisions of the Regulation of the Ministry of Interior from 17 November 2011. The correctness of the generalization principles contained in the Regulation has not yet been verified. Thus this paper aims to fulfil this gap by evaluating map specifications concerning settlement and road network generalizations. The goal was to automate the selection process by using formalized cartographic knowledge. The selection operators and their parameters were developed and implemented in the form of a generalization model. The input data was the General Geographic Object Database (GGOD), whose detail level corresponds to 1:250 000 scale. The presented research is in line with works on the automation of GGOD generalization performed by the National Mapping Agency (NMA) in Poland (GUGiK). The paper makes the following contributions. First, the selection methodology contained in the Regulation was formalised and presented in the form of a knowledge base. Second, the models for the generalization process were developed. The developed methodology was evaluated by generalizing the settlements and roads in the test area. The results of the settlement and road network generalization for both 1:500 000 and 1:1 000 000 detail levels were compared with the maps designed manually by experienced cartographers.

scholarly journals Automatic Road Segmentation from High Resolution Satellite Images using Encoder-Decoder Network

2019 ◽  
Vol 8 (10) ◽  
pp. 976-980
Keyword(s):  
High Resolution ◽  
Road Network ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Computational Overhead ◽  
Spatial Features ◽  
High Resolution Satellite Images ◽  
Spatial Pyramid Pooling ◽  
High Resolution Satellite Imagery ◽  
Road Segmentation

Road network segmentation from high resolution satellite imagery have profound applications in remote sensing. They facilitate for transportation, GPS navigation and digital cartography. Most recent advances in automatic road segmentation leverage the power of networks such as fully convolutional networks and encoder-decoder networks. The main disadvantage with these networks is that they contain deep architectures with large number of hidden layers to account for the lost spatial and localization features. This will add a significant computational overhead. It is also difficult to segment roads from other road-like features. In this paper, we propose a road segmentation architecture with an encoder and two path decoder modules. One path of the decode module approximates the coarse spatial features using upsampling network. The other path uses Atrous spatial pyramid pooling module to extract multi scale context information. Both the decoder paths are combined to fine tune the segmented road network. The experiments on the Massachusetts roads dataset show that our proposed model can produce precise segmentation results than other state-of-the-art models without being computationally expensive.

Network Selection in Wireless Heterogeneous Environment Based on Available Bandwidth Estimation

2019 ◽  
Vol 12 ◽  
Author(s):  
Kiran Ahuja ◽  
Brahmjit Singh ◽  
Rajesh Khanna
Keyword(s):  
Real Time ◽  
Estimation Error ◽  
Network Selection ◽  
Bandwidth Estimation ◽  
Heterogeneous Environment ◽  
Bootstrap Approximation ◽  
Available Bandwidth ◽  
Internet Connection ◽  
Available Bandwidth Estimation ◽  
Selection Of

Background: With the availability of multiple options in wireless network simultaneously, Always Best Connected (ABC) requires dynamic selection of the best network and access technologies. Objective: In this paper, a novel dynamic access network selection algorithm based on the real time is proposed. The available bandwidth (ABW) of each network is required to be estimated to solve the network selection problem. Method: Proposed algorithm estimates available bandwidth by taking averages, peaks, low points and bootstrap approximation for network selection. It monitors real-time internet connection and resolves the selection issue in internet connection. The proposed algorithm is capable of adapting to prevailing network conditions in heterogeneous environment of 2G, 3G and WLAN networks without user intervention. It is implemented in temporal and spatial domains to check its robustness. Estimation error, overhead, estimation time with the varying size of traffic and reliability are used as the performance metrics. Results: Through numerical results, it is shown that the proposed algorithm’s ABW estimation based on bootstrap approximation gives improved performance in terms of estimation error (less than 20%), overhead (varies from 0.03% to 83%) and reliability (approx. 99%) with respect to existing techniques. Conclusion: Our proposed methodology of network selection criterion estimates the available bandwidth by taking averages, peaks, and low points and bootstrap approximation method (standard deviation) for the selection of network in the wireless heterogeneous environment. It monitors real-time internet connection and resolves internet connections selection issue. All the real-time usage and test results demonstrate the productivity and adequacy of available bandwidth estimation with bootstrap approximation as a practical solution for consistent correspondence among heterogeneous wireless networks by precise network selection for multimedia services.

scholarly journals The use of cement concrete pavements for roads, depending on climatic conditions

2019 ◽  
Vol 13 (3) ◽  
pp. 235-240
Author(s):  
Iryna Solonenko
Keyword(s):  
European Union ◽  
Statistical Models ◽  
Road Network ◽  
The European Union ◽  
Rational Selection ◽  
Cement Concrete ◽  
The Road ◽  
Road Pavement ◽  
Operational Characteristics ◽  
Selection Of

The development of road network infrastructure is an important component of the economic development of the European Union. Updating of the road network contributes to the integration of the economies of countries into a coherent whole. The road network provides the free movement of citizens, the movement of goods and the effective implementation of various services. The increase in the length of the road network leads to an increase in the financial and material costs necessary to ensure its maintenance and repair. One of the ways to reduce costs is by strengthening the physic-mechanical and operational characteristics of the pavement due to the widespread use of cement concrete. The quality of the pavement of cement concrete depends largely on the rational selection of its composition. This allows a significant increase in the durability of road pavement. The purpose of the research was: the development of recommendations for the rational selection of the composition of the road pavement material of cement concrete, aimed at upgrading longevity, and taking into account its frost resistance grade. According to the goal, the following tasks were developed: the analyses of the climatic zones in which the road network of the European Union is located; the development of a research plan, a selection of the response function and influence factors; the study of physico-mechanical and operational characteristics of the researched material of road pavement; on the basis of the obtained data, the calculation of the complex of experimental-statistical models, which describe the physico-mechanical and operational characteristics of the road pavement material; on the basis of experimental statistical models, a method was proposed for selecting the rational compositions of the cement concrete pavement road material depending on the conditions of its application. The results presented in the article can be used in engineering and scientific practice for the selection of road pavement from cement concrete for highways.

scholarly journals Modeling of Emergency Supply Scheduling Problem Based on Reliability and Its Solution Algorithm under Variable Road Network after Sudden-Onset Disasters

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15 ◽  
Author(s):  
Tinggui Chen ◽  
Shiwen Wu ◽  
Jianjun Yang ◽  
Guodong Cong ◽  
Gongfa Li
Keyword(s):  
Road Network ◽  
Road Networks ◽  
Sudden Onset ◽  
The Road ◽  
Bee Colony ◽  
Proposed Model ◽  
Effectiveness And Efficiency ◽  
In Transit ◽  
Emergency Supply

It is common that many roads in disaster areas are damaged and obstructed after sudden-onset disasters. The phenomenon often comes with escalated traffic deterioration that raises the time and cost of emergency supply scheduling. Fortunately, repairing road network will shorten the time of in-transit distribution. In this paper, according to the characteristics of emergency supplies distribution, an emergency supply scheduling model based on multiple warehouses and stricken locations is constructed to deal with the failure of part of road networks in the early postdisaster phase. The detailed process is as follows. When part of the road networks fail, we firstly determine whether to repair the damaged road networks, and then a model of reliable emergency supply scheduling based on bi-level programming is proposed. Subsequently, an improved artificial bee colony algorithm is presented to solve the problem mentioned above. Finally, through a case study, the effectiveness and efficiency of the proposed model and algorithm are verified.

scholarly journals Boundary Loss-Based 2.5D Fully Convolutional Neural Networks Approach for Segmentation: A Case Study of the Liver and Tumor on Computed Tomography

Algorithms ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 144
Author(s):  
Yuexing Han ◽  
Xiaolong Li ◽  
Bing Wang ◽  
Lu Wang
Keyword(s):  
Image Segmentation ◽  
Spatial Information ◽  
Medical Images ◽  
Tumor Segmentation ◽  
Convolutional Networks ◽  
Learning Framework ◽  
Fully Convolutional Networks ◽  
Segmentation Accuracy ◽  
Boundary Information

Image segmentation plays an important role in the field of image processing, helping to understand images and recognize objects. However, most existing methods are often unable to effectively explore the spatial information in 3D image segmentation, and they neglect the information from the contours and boundaries of the observed objects. In addition, shape boundaries can help to locate the positions of the observed objects, but most of the existing loss functions neglect the information from the boundaries. To overcome these shortcomings, this paper presents a new cascaded 2.5D fully convolutional networks (FCNs) learning framework to segment 3D medical images. A new boundary loss that incorporates distance, area, and boundary information is also proposed for the cascaded FCNs to learning more boundary and contour features from the 3D medical images. Moreover, an effective post-processing method is developed to further improve the segmentation accuracy. We verified the proposed method on LITS and 3DIRCADb datasets that include the liver and tumors. The experimental results show that the performance of the proposed method is better than existing methods with a Dice Per Case score of 74.5% for tumor segmentation, indicating the effectiveness of the proposed method.

scholarly journals A Robust Lane Detection Model Using Vertical Spatial Features and Contextual Driving Information

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 708
Author(s):  
Wenbo Liu ◽  
Fei Yan ◽  
Jiyong Zhang ◽  
Tao Deng
Keyword(s):  
Information Exchange ◽  
Information Transfer ◽  
Spatial Information ◽  
Contextual Information ◽  
Great Influence ◽  
Light Condition ◽  
Lane Detection ◽  
Detection Model ◽  
Spatial Features ◽  
Proposed Model

The quality of detected lane lines has a great influence on the driving decisions of unmanned vehicles. However, during the process of unmanned vehicle driving, the changes in the driving scene cause much trouble for lane detection algorithms. The unclear and occluded lane lines cannot be clearly detected by most existing lane detection models in many complex driving scenes, such as crowded scene, poor light condition, etc. In view of this, we propose a robust lane detection model using vertical spatial features and contextual driving information in complex driving scenes. The more effective use of contextual information and vertical spatial features enables the proposed model more robust detect unclear and occluded lane lines by two designed blocks: feature merging block and information exchange block. The feature merging block can provide increased contextual information to pass to the subsequent network, which enables the network to learn more feature details to help detect unclear lane lines. The information exchange block is a novel block that combines the advantages of spatial convolution and dilated convolution to enhance the process of information transfer between pixels. The addition of spatial information allows the network to better detect occluded lane lines. Experimental results show that our proposed model can detect lane lines more robustly and precisely than state-of-the-art models in a variety of complex driving scenarios.

Patterned Villagescapes and Road Networks in Ancient Southwestern Amazonia

2020 ◽  
pp. 1-15
Author(s):  
Sanna Saunaluoma ◽  
Justin Moat ◽  
Francisco Pugliese ◽  
Eduardo G. Neves
Keyword(s):  
Material Culture ◽  
Unmanned Aerial Vehicle ◽  
Spatial Planning ◽  
Road Network ◽  
Road Networks ◽  
Archaeological Sites ◽  
Remotely Sensed Imagery ◽  
Aerial Vehicle ◽  
Human Transport ◽  
Southwestern Amazonia

Our recent data, collected using remotely sensed imagery and unmanned aerial vehicle surveys, reveal the extremely well-defined patterning of archaeological plaza villages in the Brazilian Acre state in terms of size, layout, chronology, and material culture. The villages comprise various earthen mounds arranged around central plazas and roads that radiate outward from, or converge on, the sites. The roads connected the villages situated 2–10 km from each other in eastern Acre. Our study attests to the existence of large, sedentary, interfluvial populations sharing the same sociocultural identities, as well as structured patterns of movement and spatial planning in relation to operative road networks during the late precolonial period. The plaza villages of Acre show similarity with the well-documented communities organized by road networks in the regions of the Upper Xingu and Llanos de Mojos. Taking into consideration ethnohistorical and ethnographic evidence, as well as the presence of comparable archaeological sites and earthwork features along the southern margin of Amazonia, we suggest that the plaza villages of Acre were linked by an interregional road network to other neighboring territories situated along the southern Amazonian rim and that movement along roads was the primary mode of human transport in Amazonian interfluves.

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