Integrating UAV Photogrammetry and Terrestrial Laser Scanning for Three-Dimensional Geometrical Modeling of Post-earthquake County of Beichuan

Xiao-xi, Chen; Jia, Dongfeng; Zhang, Weiping

doi:10.1007/978-3-030-51295-8_75

Cited by 5 publications

(5 citation statements)

References 19 publications

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“…Fusing at the prior level assigns classified images to 3D PCs, enhancing LiDAR data semantic segmentation. This approach expedites convergence and reduces loss, thanks to direct image classification [14], but has challenges with non-overlapping areas and uncertainties [15]. There is a scarcity of prior-level fusion approach-based studies in the existing literature.…”

Section: Prior-level Fusion Approachesmentioning

confidence: 99%

Investigating Prior-Level Fusion Approaches for Enriched Semantic Segmentation of Urban LiDAR Point Clouds

Ballouch,

Hajji,

Kharroubi

et al. 2024

Remote Sensing

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Three-dimensional semantic segmentation is the foundation for automatically creating enriched Digital Twin Cities (DTCs) and their updates. For this task, prior-level fusion approaches show more promising results than other fusion levels. This article proposes a new approach by developing and benchmarking three prior-level fusion scenarios to enhance the outcomes of point cloud-enriched semantic segmentation. The latter were compared with a baseline approach that used the point cloud only. In each scenario, specific prior knowledge (geometric features, classified images, or classified geometric information) and aerial images were fused into the neural network’s learning pipeline with the point cloud data. The goal was to identify the one that most profoundly enhanced the neural network’s knowledge. Two deep learning techniques, “RandLaNet” and “KPConv”, were adopted, and their parameters were modified for different scenarios. Efficient feature engineering and selection for the fusion step facilitated the learning process and improved the semantic segmentation results. Our contribution provides a good solution for addressing some challenges, particularly for more accurate extraction of semantically rich objects from the urban environment. The experimental results have demonstrated that Scenario 1 has higher precision (88%) on the SensatUrban dataset compared to the baseline approach (71%), the Scenario 2 approach (85%), and the Scenario 3 approach (84%). Furthermore, the qualitative results obtained by the first scenario are close to the ground truth. Therefore, it was identified as the efficient fusion approach for point cloud-enriched semantic segmentation, which we have named the efficient prior-level fusion (Efficient-PLF) approach.

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Section: Prior-level Fusion Approachesmentioning

confidence: 99%

Investigating Prior-Level Fusion Approaches for Enriched Semantic Segmentation of Urban LiDAR Point Clouds

Ballouch,

Hajji,

Kharroubi

et al. 2024

Remote Sensing

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“…The principle is as follows (Figure 2): Any point is selected from the imported point cloud data, and then m adjacent points are found using the KD-tree method. Then, m × 3 matrices are constructed using the m point coordinates, and the covariance matrix of the matrix is calculated (1). After completing the above process, the eigenvalues and corresponding eigenvectors are obtained according to the definition of the eigenvalues (2).…”

Section: Normal Vector Calculationsmentioning

confidence: 99%

“…With the development and popularization of laser radar technology, the use of threedimensional point cloud processing technology has become increasingly important in urban development and construction [1,2]. There are many advantages of using point cloud data in actual production; however, there are also some limitations.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Clustering for Point Cloud

Lin,

Kang,

et al. 2024

Sensors

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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.

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“…The main drawback of the proposed methodology was that it could only model the types of buildings that decompose into rectangles. Comparably, Chen et al [12] obtained the buildings' present status and their reconstruction models by integrating Terrestrial Laser Scanning (TLS) and UAV (Unmanned Aerial Vehicle) photogrammetry.…”

Section: Introductionmentioning

confidence: 99%

A Prior Level Fusion Approach for the Semantic Segmentation of 3D Point Clouds Using Deep Learning

Ballouch

Hajji²,

Poux³

et al. 2022

Remote Sensing

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Three-dimensional digital models play a pivotal role in city planning, monitoring, and sustainable management of smart and Digital Twin Cities (DTCs). In this context, semantic segmentation of airborne 3D point clouds is crucial for modeling, simulating, and understanding large-scale urban environments. Previous research studies have demonstrated that the performance of 3D semantic segmentation can be improved by fusing 3D point clouds and other data sources. In this paper, a new prior-level fusion approach is proposed for semantic segmentation of large-scale urban areas using optical images and point clouds. The proposed approach uses image classification obtained by the Maximum Likelihood Classifier as the prior knowledge for 3D semantic segmentation. Afterwards, the raster values from classified images are assigned to Lidar point clouds at the data preparation step. Finally, an advanced Deep Learning model (RandLaNet) is adopted to perform the 3D semantic segmentation. The results show that the proposed approach provides good results in terms of both evaluation metrics and visual examination with a higher Intersection over Union (96%) on the created dataset, compared with (92%) for the non-fusion approach.

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Integrating UAV Photogrammetry and Terrestrial Laser Scanning for Three-Dimensional Geometrical Modeling of Post-earthquake County of Beichuan

Cited by 5 publications

References 19 publications

Investigating Prior-Level Fusion Approaches for Enriched Semantic Segmentation of Urban LiDAR Point Clouds

Investigating Prior-Level Fusion Approaches for Enriched Semantic Segmentation of Urban LiDAR Point Clouds

Adaptive Clustering for Point Cloud

A Prior Level Fusion Approach for the Semantic Segmentation of 3D Point Clouds Using Deep Learning

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