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Improvement of mobile lidar data classification of urban road environment using machine learning algorithms / Mahmoud Abdeltawwab Abdelhamid Mohamed ; Supervised Adel Hassan Yousef Elshazly , Salem Wagih Salem Morsy

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Mahmoud Abdeltawwab Abdelhamid Mohamed , 2021Description: 61 P. : charts , facsimiles ; 30cmOther title:
  • تحسين تصنيف بيانات الليدار المحمول الخاصة ببيئة الطرق الحضرية باستخدام خوارزميات التعلم الآلى [Added title page title]
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Dissertation note: Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Civil Engineering Summary: 3D road mapping is essential for intelligent transportation system in smart cities. Road features can be utilized for road maintenance, autonomous driving vehicles, and providing regulations to drivers. Currently, 3D road environment receives its data from Mobile LIDAR Scanning (MLS) systems. MLS systems are capable of rapidly acquiring dense and accurate 3D point clouds, which allow for effective surveying of long road corridors.They produce huge amount of point clouds, which require automatic features classification algorithms with acceptable processing time. Machine learning (ML) algorithms are widely used for predicting the future or classifying information to help policymakers in making necessary decisions. This prediction comes from a pre-trained model on a given data consisting of inputs and their corresponding outputs of the same characteristics. In this research, an attempt to extract some road features from MLS point cloud using proper ML classifier, and evaluation of different steps entire the method
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Thesis Thesis قاعة الرسائل الجامعية - الدور الاول المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.05.M.Sc.2021.Ma.I (Browse shelf(Opens below)) Not for loan 01010110085325000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.05.M.Sc.2021.Ma.I (Browse shelf(Opens below)) 85325.CD Not for loan 01020110085325000

Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Civil Engineering

3D road mapping is essential for intelligent transportation system in smart cities. Road features can be utilized for road maintenance, autonomous driving vehicles, and providing regulations to drivers. Currently, 3D road environment receives its data from Mobile LIDAR Scanning (MLS) systems. MLS systems are capable of rapidly acquiring dense and accurate 3D point clouds, which allow for effective surveying of long road corridors.They produce huge amount of point clouds, which require automatic features classification algorithms with acceptable processing time. Machine learning (ML) algorithms are widely used for predicting the future or classifying information to help policymakers in making necessary decisions. This prediction comes from a pre-trained model on a given data consisting of inputs and their corresponding outputs of the same characteristics. In this research, an attempt to extract some road features from MLS point cloud using proper ML classifier, and evaluation of different steps entire the method

Issued also as CD

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