AUTOMATIC ROAD ELEMENT DETECTION FROM MOBILE LiDAR DATA USING MACHINE LEARNING

The main aim of the research is to automatically find out road features such as roadway, shoulder, cycle tracks etc. from LiDAR data, which generates a 3-dimensional point cloud, using machine-learning tools. Objective: The objectives of this research can be formulated based on the research questions and aim of the research. •To develop a methodology and classification algorithm to extract the features of the roadway using machine learning tools. •A separation between roads and other features such as shoulder, cycle track, and a pedestrian track will possible using the road characteristics in the LiDAR intensity and range data. •Machine learning tools used for automatic extractions of roadway markings with the help of previous datasets. Following points will be considered in this project- 1. Here I attached research papers for this project. 2. Based on 1st paper (Extraction of road surface from mobile LiDAR data of complex road environment), I want to use a machine learning technique based on this paper(Classification techniques). 3. A brief report around 45-50 pages on the following point: (a) Abstract (a) Introduction: 5-8 pages (b) Literature Review: 12-15 pages (c) Aim & Objectives: 2-3 pages (e) Detailed Methodology: 5-8 pages (f) Test results & Validation: 6-8 pages (h) Software requirements and Algorithm (h) References: 7-10 pages(Formatted based on APA style) 4. Report checked by plagiarism 5. Reference paper is attached for a project, based on this a report will be written. 6. I have some datasets, you can be used for the algorithm. 7. Duration: 1 to 1.5 months.