The generation of digital terrain models from LiDAR data using seeding and filtering and its application to flood modelling

Digital terrain models are foundation of many applications. Several algorithms have been developed for laser point segmentation and usually each has its advantages and drawbacks. In this study, we present a novel unsupervised algorithm for digital terrain model generation, which combines the advantages of the seed-based filtering and the slope-based filtering to classify objects and ground points. We also apply the results of digital terrain models to flood modeling. The experimental results have shown that the proposed algorithm is able to handle large buildings and steep slopes for the generation of digital terrain models.