Genetic Algorithm Empowering Unsupervised Learning for Optimizing Building Segmentation from Light Detection and Ranging Point Clouds

This study investigates the application of LiDAR point cloud datasets for building segmentation through a combined approach that integrates unsupervised segmentation with evolutionary optimization. The research evaluates the extent of improvement achievable through genetic algorithm (GA) optimization for LiDAR point cloud segmentation. The unsupervised methodology encompasses preprocessing, adaptive thresholding, morphological operations, contour filtering, and terrain ruggedness analysis. A genetic algorithm was employed to fine-tune the parameters for these techniques. Critical tunable parameters, such as the interpolation method for DSM and DTM generation, scale factor for contrast enhancement, adaptive constant and block size for adaptive thresholding, kernel size for morphological operations, squareness threshold to maintain the shape of predicted objects, and terrain ruggedness index (TRI) were systematically optimized. The study presents the top ten chromosomes with optimal parameter values, demonstrating substantial improvements of 29% in the average intersection over union (IoU) score (0.775) on test datasets. These findings offer valuable insights into LiDAR-based building segmentation, highlighting the potential for increased precision and effectiveness in future applications.