scholarly journals Scattering in remote sensing in the visible and microwave spectral range and in traffic control

2003 ◽  
Vol 1 ◽  
pp. 309-311
Author(s):  
U. Böttger ◽  
R. Kühne ◽  
K.-U. Thiessenhusene
Keyword(s):  
Complex System ◽  
Remote Sensing ◽  
Spectral Range ◽  
Satellite Data ◽  
Traffic Control ◽  
Road Network ◽  
Traffic Simulation ◽  
The Road ◽  
Traffic Area ◽  
Spectral Ranges

Abstract. The treatment of scattering processes in remote sensing for interpretation of satellite data is demonstrated in the visible and microwave spectral range comparing the two spectral ranges. Analogies and distinctions in the treatment of the scattering processes are shown. Based on this cognition an approach for traffic simulation is outlined. Simulating the traffic of a part of a city, a whole city or a larger area in an acceptable time is one of the tasks in recent traffic research. One possible approach is the areal treatment of the road network. That means that single streets are not resolved but are introduced into simulations only by parameters that correspond to a specific traffic area resistance. The aim of this work is to outline such a possibility using experiences obtained from the theory of radiative transport to simulate scattering processes and applying them to the very complex system of traffic simulation.

scholarly journals A Framework Based on Nesting of Convolutional Neural Networks to Classify Secondary Roads in High Resolution Aerial Orthoimages

2020 ◽  
Vol 12 (5) ◽  
pp. 765 ◽  
Author(s):  
Calimanut-Ionut Cira ◽  
Ramon Alcarria ◽  
Miguel-Ángel Manso-Callejo ◽  
Francisco Serradilla
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Deep Learning ◽  
High Resolution ◽  
Learning Strategies ◽  
Convolutional Neural Networks ◽  
Test Data ◽  
Road Network ◽  
Ideal Solution ◽  
The Road

Remote sensing imagery combined with deep learning strategies is often regarded as an ideal solution for interpreting scenes and monitoring infrastructures with remarkable performance levels. In addition, the road network plays an important part in transportation, and currently one of the main related challenges is detecting and monitoring the occurring changes in order to update the existent cartography. This task is challenging due to the nature of the object (continuous and often with no clearly defined borders) and the nature of remotely sensed images (noise, obstructions). In this paper, we propose a novel framework based on convolutional neural networks (CNNs) to classify secondary roads in high-resolution aerial orthoimages divided in tiles of 256 × 256 pixels. We will evaluate the framework’s performance on unseen test data and compare the results with those obtained by other popular CNNs trained from scratch.

Research of Urban Intersections Group Scoping Based on Simulation

2014 ◽  
Vol 552 ◽  
pp. 240-243 ◽  
Author(s):  
Shuo Wang ◽  
Xiao Han ◽  
Qian Wang
Keyword(s):  
Traffic Control ◽  
Traffic Congestion ◽  
Road Network ◽  
Practical Significance ◽  
Strongly Correlated ◽  
Operating Characteristics ◽  
Group Program ◽  
The Road ◽  
Dynamic Partitioning ◽  
Time Space

An urban intersection group consists of a set of intersections which are geographically adjacent and strongly correlated with each other. It is an effective way to relieve traffic congestion in the networks to set the key intersection of the road network as the core, radiate outward to find the scale of its influence and divide the intersections into groups according to their relevance. Determining intersections group is the fundament of improving traffic control. Therefore, analyzing the associated features of intersections group and the research method of determining the scope of intersection group to ensure the compatibility of "time", " space" and "traffic flow", is of high research value and practical significance[1]. This paper focuses on operating characteristics of adjacent intersections with factor (CF) as indicators of the value of the associated metric of the adjacent sections and developing dynamic partitioning intersection group program in VISSIM, and take the road network near Guangzhou Road as example.

scholarly journals Impact on Road Safety and Operation of Rerouting Traffic in Rural Travel Time Information System

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4145
Author(s):  
Mariusz Kiec ◽  
Carmelo D’Agostino ◽  
Sylwia Pazdan
Keyword(s):  
Information System ◽  
Travel Time ◽  
Traffic Control ◽  
Time Management ◽  
Road Safety ◽  
Road Network ◽  
Safety Level ◽  
The Road ◽  
Traffic Operation ◽  
Time Information

The Travel Time Information System (TTIS) is an Intelligent Traffic Control System installed in Poland. As is common, travel time is the only factor in the decision about rerouting traffic, while a route recommendation may consider multiple criteria, including road safety. The aim of the paper is to analyze the safety level of the entire road network when traffic is rerouted on paths with different road categories, intersection types, road environments, and densities of access points. Furthermore, a comparison between traffic operation and road safety performance was carried out, considering travel time and delay, and we predicted the number of crashes for each possible route. The results of the present study allow for maximizing safety or traffic operation characteristics, providing an effective tool in the management of the rural road system. The paper provides a methodology that can be transferred to other TTISs for real-time management of the road network.

scholarly journals A Study on the Coordination of Urban Traffic Control and Traffic Assignment

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
ZhaoWei Qu ◽  
Yan Xing ◽  
XianMin Song ◽  
YuZhou Duan ◽  
Fulu Wei
Keyword(s):  
Traffic Control ◽  
Network Flow ◽  
Road Network ◽  
Traffic Assignment ◽  
Route Choice ◽  
Flow Distribution ◽  
Urban Traffic ◽  
Network Efficiency ◽  
The Road ◽  
Value Model

The interactions between signal setting and traffic assignment can directly affect the urban road network efficiency. In order to improve the coordination of signal setting with traffic assignment, this paper created a traffic control algorithm considering traffic assignment; meanwhile, the link impedance function and the route choice function were introduced into this paper to study the user's route choice and the road network flow distribution. Then based on the above research, we created a system utility value model. Finally through the VISSIM software to simulate the test network, we verified the superiority of the coordination algorithm and the model and gave the optimal flow of the road network.

scholarly journals Road Extraction from High-Resolution Remote Sensing Imagery Using Deep Learning

2018 ◽  
Vol 10 (9) ◽  
pp. 1461 ◽  
Author(s):  
Yongyang Xu ◽  
Zhong Xie ◽  
Yaxing Feng ◽  
Zhanlong Chen
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Convolutional Neural Network ◽  
Road Network ◽  
Deep Convolutional Neural Network ◽  
Road Extraction ◽  
Remote Sensing Imagery ◽  
The Road

The road network plays an important role in the modern traffic system; as development occurs, the road structure changes frequently. Owing to the advancements in the field of high-resolution remote sensing, and the success of semantic segmentation success using deep learning in computer version, extracting the road network from high-resolution remote sensing imagery is becoming increasingly popular, and has become a new tool to update the geospatial database. Considering that the training dataset of the deep convolutional neural network will be clipped to a fixed size, which lead to the roads run through each sample, and that different kinds of road types have different widths, this work provides a segmentation model that was designed based on densely connected convolutional networks (DenseNet) and introduces the local and global attention units. The aim of this work is to propose a novel road extraction method that can efficiently extract the road network from remote sensing imagery with local and global information. A dataset from Google Earth was used to validate the method, and experiments showed that the proposed deep convolutional neural network can extract the road network accurately and effectively. This method also achieves a harmonic mean of precision and recall higher than other machine learning and deep learning methods.

scholarly journals Entropy-Based Approach for the Analysis of Spatio-Temporal Urban Growth Dynamics

Proceedings ◽  
2019 ◽  
Vol 46 (1) ◽  
pp. 17
Author(s):  
Garima Nautiyal ◽  
Sandeep Maithani ◽  
Ashutosh Bhardwaj ◽  
Archana Sharma
Keyword(s):  
Urban Growth ◽  
Satellite Data ◽  
Road Network ◽  
Growth Dynamics ◽  
City Center ◽  
Rapid Urbanization ◽  
The Road ◽  
Time Period ◽  
Spatio Temporal ◽  
Landsat Satellite

Relative Entropy (RE) is defined as the measure of the degree of randomness of any geographical variable (i.e., urban growth). It is an effective indicator to evaluate the patterns of urban growth, whether compact or dispersed. In the present study, RE has been used to evaluate the urban growth of Dehradun city. Dehradun, the capital of Uttarakhand, is situated in the foothills of the Himalayas and has undergone rapid urbanization. Landsat satellite data for the years 2000, 2010 and 2019 have been used in the study. Built-up cover outside municipal limits and within municipal limits was classified for the given time period. The road network and city center of the study area were also delineated using satellite data. RE was calculated for the periods 2000–2010 and 2010–2019 with respect to the road network and city center. High values of RE indicate higher levels of urban sprawl, whereas lower values indicate compactness. The urban growth pattern over a period of 19 years was examined with the help of RE.

Spectral Variability Analysis of In-Situ Hyperspectral Remote Sensing at Leaf and Branch Scales for Tree Species at Tropical Urban Forest

2015 ◽  
Vol 73 (5) ◽  
Author(s):  
W. C. Chew ◽  
A. M. S. Lau ◽  
K. D. Kanniah ◽  
N. H. Idris
Keyword(s):  
Remote Sensing ◽  
Spectral Range ◽  
Tree Species ◽  
Remote Sensing Data ◽  
Hyperspectral Remote Sensing ◽  
Spectral Variability ◽  
Variability Analysis ◽  
Sensing Data ◽  
Spectral Ranges

Spectral variability analysis has been carried out on in-situ hyperspectral remote sensing data for 20 tree species available in tropical forest in Malaysia. Five different spectral ranges have been tested to evaluate the influence of intra-species spectral variability at specific spectral range given by different spatial scales (i.e. leaf to branch scales). The degree of intra-species spectral variability was not constant among different spectral ranges where the influence of spatial scale towards intra-species spectral variability at these spectral ranges was found increasing from leaf to branch scale. The ratio of leaves to non-photosynthetic tissues has made branch scale significantly influent the intra-species spectral variability. Results have shown that a specific spectral range was species sensitive on the intra-species and inter-species spectral variability in this study. This study also suggested the use of species sensitive wavelengths extracted from specific spectral range in hyperspectral remote sensing data in order to achieve good accuracy in tree species classification.

Sign in / Sign up

Export Citation Format

Share Document