Integrating LiDAR-Based Semantic Segmentation with Text-Based Scene Descriptions for Enhanced Highway Infrastructure Analysis

This project focuses on enhancing semantic segmentation of LiDAR-acquired data by integrating text-based scene description tools. The research aims to improve the automated extraction and detailed description of road and roadside features from 3D point cloud data obtained through LiDAR scanning. The project involves developing machine learning models for precise object detection and integrating natural language processing (NLP) techniques to create comprehensive textual descriptions of the LiDAR-mapped environments. 

The outcome is expected to automate report generation for infrastructure analysis, with significant applications in transportation engineering. Additionally, the project explores advanced image processing algorithms for object detection, requiring strong coding skills and a deep understanding of machine learning and computer vision principles.