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| Title | Extraction Of Building Outlines From Airborne Lidar Point Clouds Using Line-Cnn Based On Deep Network; [采用 Line-Cnn 深度学习网络的机载点云建筑轮廓线提取] |
|---|---|
| ID_Doc | 25925 |
| Authors | Huang Y.; Zang Y.; Jiang Q.; Mi W. |
| Year | 2024 |
| Published | Journal of Geo-Information Science, 26, 9 |
| DOI | http://dx.doi.org/10.12082/dqxxkx.2024.230503 |
| Abstract | Urban 3D modeling is indispensable for digital twinning and the development of smart cities. The effective extraction of building outlines is a critical step in achieving high-precision urban modeling and 3D mapping. At present, the extraction of building outlines from airborne point cloud data still faces challenges, such as low efficiency and accuracy with conventional methods and limited calibration samples. In response to these challenges, this paper introduces a deep learning method for extracting building outlines from 3D airborne point clouds. The airborne LiDAR point clouds are the primary data input. First, through vertical projection to the XOY plane, point clouds of buildings with the application of progressive morphological filtering are converted to rasterized elevation that characterizes spatial variation of terrain and visible light raster images that depict texture differences. Then, the deep learning model based on Lines-Convolutional Neural Networks (Line-CNN) is employed to preliminarily extract line features from raster images, encompassing stages of feature extraction, node prediction, route generation, and others. To enhance the quality of the primary straight-line extraction, an optimization strategy is introduced, which incorporates a range of comprehensive trimming and completion operations, aligning with information extracted from both the elevation and visible light raster images. Simultaneously, false line segments are eliminated, and missing lines are added, resulting in the regular and complete building outline features. To verify the proposed model, the airborne point cloud data from NUIST campus and the ISPRS H3D 2019 datasets are utilized in the experiment. Our results show that the proposed method accurately and comprehensively extracts building outline features from LiDAR images, achieving an impressive average accuracy and completeness rate, both up to 90%. Furthermore, the proposed method is highly efficient and effectively addresses the challenge of insufficient 3D calibration samples in traditional methods, making it suitable for various applications, particularly large-scale urban 3D modeling and cadastral surveying. To sum up, the proposed method constitutes a significant stride in advancing urban modeling and 3D mapping. It provides a novel solution to address the challenges associated with building outline extraction, particularly within the context of smart cities and digital twins. Due to the model's high accuracy, completeness, and efficiency, our method is highly helpful for a wide range of applications in the urban planning and geospatial information fields. © 2024 China Ship Scientific Research Center. All rights reserved. |
| Author Keywords | building contour line; colored airborne point cloud; deep learning; elevation raster image; high precision urban modeling; pruning and completion optimization; visible light raster image |
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