The global digital elevation measurement (DEM) products such as SRTM DEM and GDEM have been widely used for terrain slope retrieval in forests. However, the slope estimation accuracy is generally limited due to the DEMs’ low vertical accuracy over complex forest environments. The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) mission shows excellent potential for slope estimation because of the high elevation accuracy and unique design of beam pairs. This study aimed to explore the possibility of ICESat-2 data for terrain slope retrieval in the United States forests. First, raw ICESat-2 data were processed to obtain accurate ground surfaces. Second, two different methods based on beam pairs were proposed to derive terrain slopes from the ground surfaces. Third, the estimated slopes were validated by airborne LiDAR-derived slopes and compared with SRTM-derived slopes and GDEM-derived slopes. Finally, we further explored the influence of surface topography and ground elevation error on slope estimation from ICESat-2 data. The results show that the ground surface can be accurately extracted from all scenarios of ICESat-2 data, even weak beams in the daytime, which provides the basis for terrain slope retrieval from ICESat-2 beam pairs. The estimated slope has a strong correlation with airborne LiDAR-derived slopes regardless of slope estimation methods, which demonstrates that the ICESat-2 data are appropriate for terrain slope estimation in complex forest environments. Compared with the method based on along- and across-track analysis (method 1), the method based on plane fitting of beam pairs (method 2) has a high estimation accuracy of terrain slopes, which indicates that method 2 is more suitable for slope estimation because it takes full advantage of more ground surface information. Additionally, the results also indicate that ICESat-2 performs much better than SRTM DEMs and GDEMs in estimating terrain slopes. Both ground elevation error and surface topography have a significant impact on terrain slope retrieval from ICESat-2 data, and ground surface extraction should be improved to ensure the accuracy of terrain slope retrieval over extremely complex environments. This study demonstrates for the first time that ICESat-2 has a strong capability in terrain slope retrieval. Additionally, this paper also provides effective solutions to accurately estimate terrain slopes from ICESat-2 data. The ICESat-2 slopes have many potential applications, including the generation of global slope products, the improvement of terrain slopes derived from the existing global DEM products, and the correction of vegetation biophysical parameters retrieved from space-borne LiDAR waveform data.

The complexity of terrain features poses a substantial challenge in the effective processing and application of airborne LiDAR data, particularly in regions characterized by steep slopes and diverse objects. In this paper, we propose a novel multiscale filtering method utilizing a modified 3D alpha shape algorithm to increase the ground point extraction accuracy in complex terrain. Our methodology comprises three pivotal stages: preprocessing for outlier removal and potential ground point extraction; the deployment of a modified 3D alpha shape to construct multiscale point cloud layers; and the use of a multiscale triangulated irregular network (TIN) densification process for precise ground point extraction. In each layer, the threshold is adaptively determined based on the corresponding $\alpha$. Points closer to the TIN surface than the threshold are identified as ground points. The performance of the proposed method was validated using a classical benchmark dataset provided by the ISPRS and an ultra-large-scale ground filtering dataset called OpenGF. The experimental results demonstrate that this method is effective, with an average total error and a kappa coefficient on the ISPRS dataset of 3.27% and 88.97%, respectively. When tested in the large scenarios of the OpenGF dataset, the proposed method outperformed four classical filtering methods and achieved accuracy comparable to that of the best of learning-based methods.

Powerlines are critical components of powerline corridors, essential for ensuring a stable and reliable power supply. With the increasing adoption of airborne laser scanning (ALS) in corridor inspections, the demand for efficient and automated methods to extract powerline points from point clouds has grown significantly. This paper presents a method for automated powerline extraction from ALS-acquired point clouds, addressing key challenges such as large-scale data processing, complex terrains, and precise segmentation of powerlines and associated components. The proposed approach begins with gridbased pylon location detection and span-based organization of massive powerline corridor data, enabling efficient data processing. A robust segmentation technique is then introduced, transforming the task into a connected component identification problem within a mesh generated using the 3D Alpha Wrapping algorithm to effectively separate powerlines from other objects. To address irregular gaps in powerline point clouds, a graphcut-based energy minimization method is developed to identify and merge powerline fragments. Furthermore, a novel slice-based segmentation technique is introduced to accurately differentiate powerlines from components such as pylons, insulator strings, and jumper wires. The method was evaluated on nine datasets spanning 80.32 km, covering voltage levels of 220 kV, 500 kV, and 800 kV. It achieved an average recall of 0.9981, precision of 0.9977, and F1 score of 0.9979, demonstrating its robustness, adaptability, and high performance across diverse scenarios.

Cylinder point extraction is a crucial prerequisite for applications of industrial scenarios, such as reverse engineering and clearance analysis, using laser point clouds. This task currently suffers from data missing, noise, and interference from the planar objects. The generalization capability for cylindrical objects with different radii is also required. Considering the cylindrical object as the continuously extended cylinder, a region growth algorithm using a multi-scale prospecting strategy is proposed in this paper. Firstly, the preprocess is conducted to subsample the point cloud and estimate each point normal. Secondly, the kernel computation is used to detect the potential cylinder patches as kernels with the multi-scale cylinder prospecting. Then, the rough extraction is performed to compute the cylinder parameters with the iterative growth starting from kernels. Finally, the fine extraction labels the raw point cloud by assigning points to different cylinders under the length constraint. The real-world point clouds, which are collected from four industrial scenarios, the simulated point cloud, and an open dataset are employed to evaluate the proposed method performance. Experimental results demonstrate the effectiveness and robustness of the proposed method. The average precision, recall, and F1-Score on the real-world dataset and open dataset are 0.879, 0.894, 0.885 and 0.931, 0.893, 0.909, respectively.

Semantic segmentation of transmission-line point clouds is fundamental to intelligent power inspection and grid asset management, as segmentation accuracy directly influences defect detection and facility assessment tasks. However, transmission-line point clouds collected across different voltage levels often show significant variations in density and geometric structure due to heterogeneous LiDAR sensors and flight configurations. Combined with the high cost of large-scale manual annotation, these factors limit the scalability of existing supervised segmentation methods. To overcome these challenges, we propose a geometry-consistency-guided unsupervised domain adaptation framework tailored for cross-voltage transmission-line point-cloud segmentation. The framework employs KPConvX as the backbone and integrates three progressive components. First, a geometric consistency constraint enhances robustness to spatial variations and enables extraction of structural features invariant across voltage levels. Second, a domain feature alignment module reduces distribution shifts through global feature transformation. Third, a minimum-entropy-based pseudo-label refinement strategy improves the reliability of pseudo-labels during self-training. Experiments on a multi-voltage transmission-line dataset demonstrate the effectiveness of the proposed method. With the KPConvX backbone, the framework achieves 66.1% mean Intersection over Union (mIoU) and 94.3% overall accuracy on the unlabeled 110 kV target domain, exceeding the source-only baseline by 15.6% mIoU and outperforming several state-of-the-art UDA methods. This work provides an efficient, annotation-friendly solution for cross-voltage point-cloud segmentation and offers a promising direction for domain adaptation in complex power-grid environments.