An Object-Based Ground Filtering of Airborne LiDAR Data for Large-Area DTM Generation

Abstract

Digital terrain model (DTM) creation is a modeling process that represents the Earth’s surface. An aptly designed DTM generation method tailored for intended study can significantly streamline ensuing processes and assist in managing errors and uncertainties, particularly in large-area projects. However, existing methods often exhibit inconsistent and inexplicable results, struggle to clearly define what an object is, and often fail to filter large objects due to their locally confined operations. We introduce a new DTM generation method that performs object-based ground filtering, which is particularly beneficial for urban topography. This method defines objects as areas fully enclosed by steep slopes and grounds as smoothly connected areas, enabling reliable “object-based” segmentation and filtering, extending beyond the local context. Our primary operation, controlled by a slope threshold parameter, simplifies tuning and ensures predictable results, thereby reducing uncertainties in large-area modeling. Uniquely, our method considers surface water bodies in modeling and treats connected artificial terrains (e.g., overpasses) as ground. This contrasts with conventional methods, which often create noise near water bodies and behave inconsistently around overpasses and bridges, making our approach particularly beneficial for large-area 3D urban mapping. Examined on extensive and diverse datasets, our method offers unique features and high accuracy, and we have thoroughly assessed potential artifacts to guide potential users.