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Laser Scanning Systems in Highway and Safety Assessment

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Cover of 'Laser Scanning Systems in Highway and Safety Assessment'

Table of Contents

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    Book Overview
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    Chapter 1 Laser Scanning Technologies in Road Geometry Modeling
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    Chapter 2 Road Geometric Modeling Using Laser Scanning Data: A Critical Review
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    Chapter 3 Road Geometric Modeling Using a Novel Hierarchical Approach
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    Chapter 4 Optimizing Support Vector Machine and Ensemble Trees Using Taguchi Method for Road Extraction from LiDAR Data
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    Chapter 5 An Integrated Machine Learning Approach for Automatic Highway Extraction from Airborne LiDAR Data and Orthophotos
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    Chapter 6 Effect of Roadside Features on Injury Severity of Traffic Accidents
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    Chapter 7 Novel GIS-Based Model for Automatic Identification of Road Geometry in Vector Data
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    Chapter 8 Review of Traffic Accident Predictions with Neural Networks
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    Chapter 9 Modeling Traffic Accident Severity Using Neural Networks and Support Vector Machines
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    Chapter 10 Predicting Injury Severity of Road Traffic Accidents Using a Hybrid Extreme Gradient Boosting and Deep Neural Network Approach
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    Chapter 11 Applications of Deep Learning in Severity Prediction of Traffic Accidents
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    Chapter 12 Forecasting Severity of Motorcycle Crashes Using Transfer Learning
Attention for Chapter 5: An Integrated Machine Learning Approach for Automatic Highway Extraction from Airborne LiDAR Data and Orthophotos
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Citations

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Chapter title
An Integrated Machine Learning Approach for Automatic Highway Extraction from Airborne LiDAR Data and Orthophotos
Chapter number 5
Book title
Laser Scanning Systems in Highway and Safety Assessment
Published by
Springer, Cham, January 2020
DOI 10.1007/978-3-030-10374-3_5
Book ISBNs
978-3-03-010373-6, 978-3-03-010374-3
Authors

Biswajeet Pradhan, Maher Ibrahim Sameen

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 44%
Researcher 2 22%
Other 1 11%
Unknown 2 22%
Readers by discipline Count As %
Engineering 3 33%
Computer Science 2 22%
Earth and Planetary Sciences 1 11%
Unknown 3 33%

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