Detect Traffic Lane Image Using Geospatial LiDAR Data Point Clouds with Machine Learning Analysis

Abstract

Artificial intelligence is a challenging domain in geospatial technology. It will boost the heights in various application domains while also displaying the variance in the geographical concept. Artificial intelligence-based techniques are crucial in LiDAR evaluation and geospatial digital images to interpret the components of geospatial AI. LiDAR point clouds technique will explain the feasibility of machine learning and deep learning approaches in the geospatial field. We define the workflow using LiDAR point clouds based on machine learning/deep learning approaches that will create the LiDAR point clouds in spatial LiDAR models. A regionally weighted regression includes the land-use/land-cover change indicator and the geospatial weighted regression (GWR). Machine learning and deep learning enable the LiDAR technique in geospatial to build and maintain all virtual models. We use traffic images to detect the conjunction, collision, and crowd traffic using the LiDAR technique. This research gives the accuracy of images using machine learning concepts.