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040 _aEG-GiCUC
_beng
_cEG-GiCUC
041 0 _aeng
049 _aDeposite
097 _aM.Sc
099 _aCai01.13.05.M.Sc.2021.Ma.I
100 0 _aMahmoud Abdeltawwab Abdelhamid Mohamed
245 1 0 _aImprovement of mobile lidar data classification of urban road environment using machine learning algorithms /
_cMahmoud Abdeltawwab Abdelhamid Mohamed ; Supervised Adel Hassan Yousef Elshazly , Salem Wagih Salem Morsy
246 1 5 _aتحسين تصنيف بيانات الليدار المحمول الخاصة ببيئة الطرق الحضرية باستخدام خوارزميات التعلم الآلى
260 _aCairo :
_bMahmoud Abdeltawwab Abdelhamid Mohamed ,
_c2021
300 _a61 P. :
_bcharts , facsimiles ;
_c30cm
502 _aThesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Civil Engineering
520 _a3D 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
530 _aIssued also as CD
653 4 _aClassification
653 4 _aMobile liDAR data
653 4 _aNeighborhood
700 0 _aAdel Hassan Yousef Elshazly ,
_eSupervisor
700 0 _aSalem Wagih Salem Morsy ,
_eSupervisor
856 _uhttp://172.23.153.220/th.pdf
905 _aNazla
_eRevisor
905 _aShimaa
_eCataloger
942 _2ddc
_cTH
999 _c84082
_d84082