scholarly journals AUTOMATIC EXTRACTION OF ROAD CENTERLINES AND EDGE LINES FROM AERIAL IMAGES VIA CNN-BASED REGRESSION

2020 ◽  
Vol V-2-2020 ◽  
pp. 925-932
Author(s):  
Y. Wei ◽  
X. Hu ◽  
M. Zhang ◽  
Y. Xu
Keyword(s):  
Road Network ◽  
Aerial Images ◽  
Automatic Extraction ◽  
Tensor Voting ◽  
Road Extraction ◽  
The Road ◽  
Road Width ◽  
Road Intersections ◽  
On The Road ◽  
Segmentation Problem

Abstract. Extracting roads from aerial images is a challenging task in the field of remote sensing. Most approaches formulate road extraction as a segmentation problem and use thinning and edge detection to obtain road centerlines and edge lines, which could produce spurs around the extracted centerlines/edge lines. In this study, a novel regression-based method is proposed to extract road centerlines and edge lines directly from aerial images. The method consists of three major steps. First, an end-to-end regression network based on CNN is trained to predict confidence maps for road centerlines and estimate road width. Then, after the CNN predicts the confidence map, non-maximum suppression and road tracking are applied to extract accurate road centerlines and construct road topology. Meanwhile, Road edge lines are generated based on the road width estimated by the CNN. Finally, in order to improve the connectivity of extracted road network, tensor voting is applied to detect road intersections and the detected intersections are used as guidance for the overcome of discontinuities. The experiments conducted on the SpaceNet and DeepGlobe datasets show that our approach achieves better performance than other methods.

scholarly journals IMPACT OF GAP FILLING ON QUALITY OF ROAD NETWORKS

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 683-686
Author(s):  
M. Maboudi ◽  
J. Amini ◽  
M. Gerke
Keyword(s):  
Road Network ◽  
Road Networks ◽  
Quality Criteria ◽  
Tensor Voting ◽  
Road Extraction ◽  
Gap Filling ◽  
The Road ◽  
Geometrical Quality ◽  
On The Road

Abstract. High quality and updated road network maps provide important information for many domains. Many small segments appear on the road surface in VHR images. Most road extraction systems have problem in extraction of these small segments and usually they appear as gaps in the final extracted road networks. However, most approaches skip filling these gaps. This is on account of the fact that usually overall length of the missing parts of the road extraction results is very short relative to the total length of the whole road network. This leads to an indiscernible impact of filling these gaps on geometrical quality criteria. In this paper, using two different VHR satellite datasets and a gap-filling approach which is based on tensor voting, we show that utilizing an effective road gap filling can result in a quite tangible topological improvement in the final road network which is highly demanded in many applications.

scholarly journals With Geospatial in Path of Smart City

2015 ◽  
Vol XL-7/W3 ◽  
pp. 1381-1387 ◽  
Author(s):  
A. S. Homainejad
Keyword(s):  
Information And Communication Technologies ◽  
Smart City ◽  
Traffic Management ◽  
Road Network ◽  
Aerial Images ◽  
Transport Systems ◽  
City Management ◽  
Traffic Jam ◽  
The Road ◽  
On The Road

With growth of urbanisation, there is a requirement for using the leverage of smart city in city management. The core of smart city is Information and Communication Technologies (ICT), and one of its elements is smart transport which includes sustainable transport and Intelligent Transport Systems (ITS). Cities and especially megacities are facing urgent transport challenge in traffic management. Geospatial can provide reliable tools for monitoring and coordinating traffic. In this paper a method for monitoring and managing the ongoing traffic in roads using aerial images and CCTV will be addressed. In this method, the road network was initially extracted and geo-referenced and captured in a 3D model. The aim is to detect and geo-referenced any vehicles on the road from images in order to assess the density and the volume of vehicles on the roads. If a traffic jam was recognised from the images, an alternative route would be suggested for easing the traffic jam. In a separate test, a road network was replicated in the computer and a simulated traffic was implemented in order to assess the traffic management during a pick time using this method.

scholarly journals Towards Automatic Extraction and Updating of VGI-Based Road Networks Using Deep Learning

2019 ◽  
Vol 11 (9) ◽  
pp. 1012 ◽  
Author(s):  
Prajowal Manandhar ◽  
Prashanth Reddy Marpu ◽  
Zeyar Aung ◽  
Farid Melgani
Keyword(s):  
Deep Learning ◽  
United Arab Emirates ◽  
Road Network ◽  
Aerial Images ◽  
Automatic Extraction ◽  
The Road ◽  
Second Stage ◽  
Road Map ◽  
Road Network Extraction ◽  
Multi Agent

This work presents an approach to road network extraction in remote sensing images. In our earlier work, we worked on the extraction of the road network using a multi-agent approach guided by Volunteered Geographic Information (VGI). The limitation of this VGI-only approach is its inability to update the new road developments as it only follows the VGI. In this work, we employ a deep learning approach to update the road network to include new road developments not captured by the existing VGI. The output of the first stage is used to train a Convolutional Neural Network (CNN) in the second stage to generate a general model to classify road pixels. Post-processing is used to correct the undesired artifacts such as buildings, vegetation, occlusions, etc. to generate a final road map. Our proposed method is tested on the satellite images acquired over Abu Dhabi, United Arab Emirates and the aerial images acquired over Massachusetts, United States of America, and is observed to produce accurate results.

scholarly journals AUTOMATIC DETECTION AND RECOGNITION OF ROAD INTERSECTIONS FOR ROAD EXTRACTION FROM IMAGERY

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 113-117
Author(s):  
P. Li ◽  
Y. Li ◽  
J. Feng ◽  
Z. Ma ◽  
X. Li
Keyword(s):  
Urban Areas ◽  
Road Network ◽  
Feature Vector ◽  
Region Of Interest ◽  
Road Extraction ◽  
Geometric Information ◽  
The Road ◽  
Contour Shape ◽  
Intersection Area ◽  
Road Intersections

Abstract. Automatic road extraction from remote sensing imagery is very useful for many applications involved with geographic information. For road extraction of urban areas, road intersections offer stable and reliable information for extraction of road network, with higher completeness and accuracy. In this paper, a segmentation-shape analysis based method is proposed to detect road intersections and their branch directions from an image. In the region of interest, it uses the contour shape of the segmented-intersection area to form a feature vector representing its geometric information. The extracted feature vector is then matched with some template vectors in order to find the best matched intersection pattern, obtain the type of intersection and the direction of connected roads. The experimental analysis are carried out with ISPRS Vaihingen and Toronto images. The experimental results show that the proposed method can extract most of the road intersections correctly. For the Vaihingen image, the the completeness and correctness are 81% and 87%, respectfully, while for the Toronto image, the the completeness and correctness are 78% and 85%, respectfully. It can help to build more correct and complete road network.

scholarly journals Politics, industry, and tourism: The conceptual construction of the blue highway

2020 ◽  
pp. 002252662097950
Author(s):  
Fredrik Bertilsson
Keyword(s):  
Road Network ◽  
Power Plants ◽  
Community Building ◽  
Atlantic Coast ◽  
Political Agenda ◽  
The Road ◽  
Conceptual Construction ◽  
On The Road ◽  
Post War

This article contributes to the research on the expansion of the Swedish post-war road network by illuminating the role of tourism in addition to political and industrial agendas. Specifically, it examines the “conceptual construction” of the Blue Highway, which currently stretches from the Atlantic Coast of Norway, traverses through Sweden and Finland, and enters into Russia. The focus is on Swedish governmental reports and national press between the 1950s and the 1970s. The article identifies three overlapping meanings attached to the Blue Highway: a political agenda of improving the relationships between the Nordic countries, industrial interests, and tourism. Political ambitions of Nordic community building were clearly pronounced at the onset of the project. Industrial actors depended on the road for the building of power plants and dams. The road became gradually more connected with the view of tourism as the motor of regional development.

scholarly journals THE PENETRATION OF ENGINEERING BY ECONOMICS: McFADDEN AND THE TRANSFORMATION OF ROAD DEMAND ESTIMATION

2021 ◽  
Vol 43 (2) ◽  
pp. 262-278
Author(s):  
Ariane Dupont-Kieffer ◽  
Sylvie Rivot ◽  
Jean-Loup Madre
Keyword(s):  
Road Network ◽  
Road Construction ◽  
Demand Estimation ◽  
Demand Modeling ◽  
The Road ◽  
Collective Decisions ◽  
The Face ◽  
Decision Making Processes ◽  
On The Road ◽  
The 1960S

The golden age of road demand modeling began in the 1950s and flourished in the 1960s in the face of major road construction needs. These macro models, as well as the econometrics and the data to be processed, were provided mainly by engineers. A division of tasks can be observed between the engineers in charge of estimating the flows within the network and the transport economists in charge of managing these flows once they are on the road network. Yet the inability to explain their decision-making processes and individual drives gave some room to economists to introduce economic analysis, so as to better understand individual or collective decisions between transport alternatives. Economists, in particular Daniel McFadden, began to offer methods to improve the measure of utility linked to transport and to inform the engineering approach. This paper explores the challenges to the boundaries between economics and engineering in road demand analysis.

scholarly journals Measures to optimize traffic and the environment. Calculations of environmental safety on a given section of the Rostov-on-Don road network

2020 ◽  
pp. 68-77
Author(s):  
R. S. Durov ◽  
◽  
E. V. Varnakova ◽  
K. O. Kobzev ◽  
◽  
...  
Keyword(s):  
Road Network ◽  
Road Transport ◽  
Environmental Safety ◽  
Environmental Indicators ◽  
Nox Emissions ◽  
The Road ◽  
Before And After ◽  
Reduce Emissions ◽  
On The Road ◽  
State Of The Environment

Introduction. One of the most pressing socio-economic problems is the state of the environment, which affects the living conditions of many people. The article deals with the problem areas of the intersection of 20-ya Liniya street – Sholokhov Avenue in Rostov-on-Don. Problem Statement. The purpose of this paper is to improve environmental safety at the intersection of 20-ya Liniya street – Sholokhov Avenue in Rostov-on-Don by reducing emissions from road transport through the proposed measures to reorganize traffic on this section of the road network. Theoretical Part. The article provides an assessment of environmental and road safety on the road network section before applying the proposed measures. The measures are listed and justified that would help improve the conditions for road transport at the selected intersection and reduce emissions from road transport, which would improve environmental safety. The calculation of environmental indicators was made after the proposed measures to reduce NOx emissions by cars. Conclusion. The article analyzes the environmental indicators before and after the events, and then compares them. Based on the analysis and calculations, it is determined how much the proposed measures to optimize traffic will help reduce NOx emissions by cars.

scholarly journals CLASSICAL PHOTOGRAMMETRY AND UAV – SELECTED ASCPECTS

2016 ◽  
Vol XLI-B1 ◽  
pp. 947-952 ◽  
Author(s):  
S. Mikrut
Keyword(s):  
Aerial Images ◽  
Relevant Research ◽  
The Road ◽  
Advantages And Disadvantages ◽  
Subpixel Accuracy ◽  
Low Costs ◽  
On The Road ◽  
Uav Images ◽  
The One ◽  
Research Centres

The UAV technology seems to be highly future-oriented due to its low costs as compared to traditional aerial images taken from classical photogrammetry aircrafts. The AGH University of Science and Technology in Cracow - Department of Geoinformation, Photogrammetry and Environmental Remote Sensing focuses mainly on geometry and radiometry of recorded images. Various scientific research centres all over the world have been conducting the relevant research for years. The paper presents selected aspects of processing digital images made with the UAV technology. It provides on a practical example a comparison between a digital image taken from an airborne (classical) height, and the one made from an UAV level. In his research the author of the paper is trying to find an answer to the question: to what extent does the UAV technology diverge today from classical photogrammetry, and what are the advantages and disadvantages of both methods? The flight plan was made over the Tokarnia Village Museum (more than 0.5 km<sup>2</sup>) for two separate flights: the first was made by an UAV - System FT-03A built by FlyTech Solution Ltd. The second was made with the use of a classical photogrammetric Cesna aircraft furnished with an airborne photogrammetric camera (Ultra Cam Eagle). Both sets of photographs were taken with pixel size of about 3 cm, in order to have reliable data allowing for both systems to be compared. The project has made aerotriangulation independently for the two flights. The DTM was generated automatically, and the last step was the generation of an orthophoto. The geometry of images was checked under the process of aerotriangulation. To compare the accuracy of these two flights, control and check points were used. RMSE were calculated. The radiometry was checked by a visual method and using the author's own algorithm for feature extraction (to define edges with subpixel accuracy). After initial pre-processing of data, the images were put together, and shown side by side. Buildings and strips on the road were selected from whole data for the comparison of edges and details. The details on UAV images were not worse than those on classical photogrammetric ones. One might suppose that geometrically they also were correct. The results of aerotriangulation prove these facts, too. Final results from aerotriangulation were on the level of RMS = 1 pixel (about 3 cm). In general it can be said that photographs from UAVs are not worse than classic ones. In the author's opinion, geometric and radiometric qualities are at a similar level for this kind of area (a small village). This is a very significant result as regards mapping. It means that UAV data can be used in mapping production.

scholarly journals Two-Layer Routing for High-Voltage Powerline Inspection by Cooperated Ground Vehicle and Drone

2019 ◽  
Author(s):  
Yao Liu ◽  
Jianmai Shi ◽  
Zhong Liu ◽  
Jincai Huang ◽  
Tianren Zhou
Keyword(s):  
High Voltage ◽  
Road Network ◽  
Inspection System ◽  
Large Area ◽  
Technical Improvement ◽  
Routing Problem ◽  
Ground Vehicle ◽  
The Road ◽  
Battery Capacity ◽  
On The Road

A novel high-voltage powerline inspection system is investigated, which consists of the cooperated ground vehicle and drone. The ground vehicle acts as a mobile platform that can launch and recycle the drone, while the drone can fly over the powerline for inspection within limited endurance. This inspection system enables the drone to inspect powerline networks in a very large area. Both vehicle&rsquo; route in the road network and drone&rsquo;s routes along the powerline network have to be optimized for improving the inspection efficiency, which generates a new two-layer point-arc routing problem. Two constructive heuristics are designed based on &ldquo;Cluster First, Rank Second&rdquo; and &ldquo;Rank First, Split Second&rdquo;. Then local search strategies are developed to further improve the quality of the solution. To test the performance of the proposed algorithms, practical cases with different-scale are designed based on the road network and powerline network of Ji&rsquo;an, China. Sensitivity analysis on the parameters related with the drone&rsquo;s inspection speed and battery capacity is conducted. Computational results indicate that technical improvement on the inspection sensor is more important for the cooperated ground vehicle and drone system.

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