Building and Vegetation Classification from Airborne Laser Scanning Point Cloud

This research presents a robust approach for building and vegetation classification from airborne laser scanning point cloud data. Using machine learning algorithms and data processing techniques, the main objective is to accurately differentiate various terrain types, such as ground surfaces, vegetation, buildings, and water bodies. The proposed method incorporates feature extraction, point cloud segmentation, and classification stages to achieve high accuracy and efficiency. Experimental results demonstrate the approach’s effectiveness in diverse geographical regions and highlight its potential applications in environmental monitoring, urban planning, and disaster management.