Youjian Hu

TL;DR: Experimental results show that the proposed improved morphological algorithm based on multi-level kriging interpolation can achieve promising results not only in flat urban areas but also in rural areas.

TL;DR: Li et al. as discussed by the authors proposed a novel method to extract road centerlines from airborne LiDAR point clouds, which is mainly composed of three key algorithms, namely, Skewness balancing, Rotating neighborhood, and Hierarchical fusion and optimization (SRH).

TL;DR: A threshold-free filtering algorithm based on expectation–maximization (EM), developed based on the assumption that point clouds are seen as a mixture of Gaussian models, which performed the best in comparison with the classic progressive triangulated irregular network densification (PTD) methods in terms of omission error.

TL;DR: The results indicate that the estimated BDS-3 DCBs have a good agreement with the products provided by the Chinese Academy of Sciences and Deutsche Zentrum fur Luft- und Raumfahrt (DLR), and there is no evident systematic bias between BDS-2 and BDS- 2 + BDS- 3 DCB.

TL;DR: This paper proposes a point cloud filtering method based on active learning that can achieve the smallest average total error and performs very well toward different terrain environments.