Mathematical vector framework for gravity-specific land surface curvatures calculation from triangulated irregular networks

Hu, Guanghui; Xiong, Liyang; Lu, Shuijing; Li, Sijin; Tang, Guoan; Strobl, Josef

doi:10.1080/15481603.2022.2044149

Cited by 10 publications

(4 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Existing normal vector estimation methods fall into three types: those based on the Delaunay method of triangulation, those based on the robust statistical method, and those based on the local surface fitting method [19][20][21]. In this paper, the local surface fitting method is selected.…”

Section: Normal Vector Calculationsmentioning

confidence: 99%

Adaptive Clustering for Point Cloud

Lin,

Kang,

et al. 2024

Sensors

View full text Add to dashboard Cite

The point cloud segmentation method plays an important role in practical applications, such as remote sensing, mobile robots, and 3D modeling. However, there are still some limitations to the current point cloud data segmentation method when applied to large-scale scenes. Therefore, this paper proposes an adaptive clustering segmentation method. In this method, the threshold for clustering points within the point cloud is calculated using the characteristic parameters of adjacent points. After completing the preliminary segmentation of the point cloud, the segmentation results are further refined according to the standard deviation of the cluster points. Then, the cluster points whose number does not meet the conditions are further segmented, and, finally, scene point cloud data segmentation is realized. To test the superiority of this method, this study was based on point cloud data from a park in Guilin, Guangxi, China. The experimental results showed that this method is more practical and efficient than other methods, and it can effectively segment all ground objects and ground point cloud data in a scene. Compared with other segmentation methods that are easily affected by parameters, this method has strong robustness. In order to verify the universality of the method proposed in this paper, we test a public data set provided by ISPRS. The method achieves good segmentation results for multiple sample data, and it can distinguish noise points in a scene.

show abstract

Section: Normal Vector Calculationsmentioning

confidence: 99%

Adaptive Clustering for Point Cloud

Lin,

Kang,

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Concave terrains represent the areas where the slope gradient gradually flattens as it descends, while convex terrains contain the reverse shape. The classification of concave and convex terrains is performed based on the curvature and slope gradient that expresses the local topographic characteristics, and the segmented units usually represent the slope surface with a relatively small area [25,27,28]. For example, the researchers of [27] updated the calculation framework of surface curvature to improve the classification of concave and convex areas, and Geomorphons [26] considered ternary patterns of landforms to represent surface morphologies.…”

Section: Introductionmentioning

confidence: 99%

“…The classification of concave and convex terrains is performed based on the curvature and slope gradient that expresses the local topographic characteristics, and the segmented units usually represent the slope surface with a relatively small area [25,27,28]. For example, the researchers of [27] updated the calculation framework of surface curvature to improve the classification of concave and convex areas, and Geomorphons [26] considered ternary patterns of landforms to represent surface morphologies. Positive and negative terrains (P-N terrains) are another expression of the binary terrain structure [23,29].…”

Section: Introductionmentioning

confidence: 99%

Quantification of Surface Pattern Based on the Binary Terrain Structure in Mountainous Areas

Yang

Zhou

et al. 2023

Remote Sensing

Self Cite

View full text Add to dashboard Cite

Terrain significantly influences the physical processes and human activities occurring on the Earth’s surface, especially in mountainous areas. The classification and clarification of topographic structures are essential for the quantitative analysis of surface patterns. In this paper, we propose a new method based on the digital elevation model to classify the binary terrain structure. The slope accumulation is constructed to emphasize the accumulated topographic characteristics and is applied to support the segmenting process. The results show that this new method is efficient in increasing the completeness of the segmented results and reducing the classification uncertainty. We verify this method in three areas in South America, North America and Asia to evaluate the method’s robustness. Comparison experiments suggest that this new method outperforms the traditional method in areas with different landforms. In addition, quantitative indices are calculated based on the segmented results. The results indicate that the binary terrain structure benefits the understanding of surface patterns from the perspectives of topographic characteristics, category composition, object morphology and landform spatial distribution. We also assess the transferability of the proposed method, and the results suggest that this method is transferable to different digital elevation models. The proposed method can support the quantitative analysis of land resources, especially in mountainous areas and benefit land management.

show abstract

“…These discrete points, which can be geographical coordinate points or pixels in color space, can represent quantifiable and meaningful discrete points, such as mass and temperature. These points also have an extremely broad development space [21] and show promising applications in 3D geological modeling. For example, in the 3D modeling of pore groundwater, the TIN spatial discrete mesh is an important representation and data organization form of the 3D solid structure model.…”

Section: Introductionmentioning

confidence: 99%

A Front Advancing Adaptive Triangular Mesh Dynamic Generation Algorithm and Its Application in 3D Geological Modeling

Lei

et al. 2023

Sustainability

View full text Add to dashboard Cite

The traditional advancing front technique algorithm encounters many problems due to the complex geometric characteristics of the front edge shape. These problems include poor quality, a slow algorithm, low robustness, and the inability of the mesh unit to converge. To address these problems, an optimized adaptive triangular mesh dynamic generation algorithm called R-TIN is proposed and applied to 3D engineering geological modeling in this study. Firstly, all the shapes involved in advancing the front edge inward were classified into four types, and then the optimal triangular unit was constructed by using the candidate mesh point heuristic algorithm. Then, the robustness of this algorithm could be maintained by the graded concession of the included angle threshold in the adjacent front-line segments. Finally, based on 160 engineering geological boreholes in the study area, the 3D engineering geological model was constructed and the accuracy and visualization effect of the overall geological model have been greatly improved, which can better present the spatial distribution of strata and lithological characteristics. At the same time, this algorithm can be used in geoscience information services to support the regional or national exploration of resources and energy, sustainable development and utilization, environmental protection and the prevention of geological disasters.

show abstract

Mathematical vector framework for gravity-specific land surface curvatures calculation from triangulated irregular networks

Cited by 10 publications

References 59 publications

Adaptive Clustering for Point Cloud

Adaptive Clustering for Point Cloud

Quantification of Surface Pattern Based on the Binary Terrain Structure in Mountainous Areas

A Front Advancing Adaptive Triangular Mesh Dynamic Generation Algorithm and Its Application in 3D Geological Modeling

Contact Info

Product

Resources

About