Vegetation segmentation using oblique photogrammetry point clouds based on RSPT network

Vegetation segmentation via point cloud data can provide important information for urban planning and environmental protection. The point cloud dataset is obtained using light detection and ranging (LiDAR) or RGB-D images. Oblique photogrammetry has received little attention as another important sou...

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Published inInternational journal of digital earth Vol. 17; no. 1
Main Authors Hu, Hong, Sun, Zhangyu, Kang, Ruihong, Wu, Yanlan, Wang, Baoguo
Format Journal Article
LanguageEnglish
Published Abingdon Taylor & Francis 31.12.2024
Taylor & Francis Ltd
Taylor & Francis Group
Subjects
Accuracy
Aggregation
China
data collection
Datasets
Environmental protection
Image processing
Image segmentation
Lidar
Oblique photogrammetry
Photogrammetry
point cloud
Random sampling
RSPT
self-attention
Semantic segmentation
Statistical sampling
texture
Urban planning
Vegetation
vegetation segmentation
China
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Abstract Vegetation segmentation via point cloud data can provide important information for urban planning and environmental protection. The point cloud dataset is obtained using light detection and ranging (LiDAR) or RGB-D images. Oblique photogrammetry has received little attention as another important source of point cloud data. We present a pointwise annotated oblique photogrammetry point-cloud dataset that contains rich RGB information, texture, and structural features. This dataset contains five regions of Bengbu, China, with more than twenty thousand samples in this paper. Obviously, previous indoor point cloud semantic segmentation models are no longer applicable to oblique photogrammetry point clouds. A random sampling point transformer (RSPT) network is proposed to enhance vegetation segmentation accuracy. The RSPT model offers both efficient and lightweight architecture. In RSPT, random point sampling is utilized to downsample point clouds, and a local feature aggregation module based on self-attention is designed to extract additional representation features. The network also incorporated residual and dense connections (ResiDense) to capture both local and comprehensive features. Compared to state-of-the-art models, RSPT achieves notable improvements. The intersection over union (IoU) metric increased from 96.0% to 96.5%, the F1-score increased from 90.8% to 97.0%, and the overall accuracy (OA) increased from 91.9% to 96.9%.
AbstractList Vegetation segmentation via point cloud data can provide important information for urban planning and environmental protection. The point cloud dataset is obtained using light detection and ranging (LiDAR) or RGB-D images. Oblique photogrammetry has received little attention as another important source of point cloud data. We present a pointwise annotated oblique photogrammetry point-cloud dataset that contains rich RGB information, texture, and structural features. This dataset contains five regions of Bengbu, China, with more than twenty thousand samples in this paper. Obviously, previous indoor point cloud semantic segmentation models are no longer applicable to oblique photogrammetry point clouds. A random sampling point transformer (RSPT) network is proposed to enhance vegetation segmentation accuracy. The RSPT model offers both efficient and lightweight architecture. In RSPT, random point sampling is utilized to downsample point clouds, and a local feature aggregation module based on self-attention is designed to extract additional representation features. The network also incorporated residual and dense connections (ResiDense) to capture both local and comprehensive features. Compared to state-of-the-art models, RSPT achieves notable improvements. The intersection over union (IoU) metric increased from 96.0% to 96.5%, the F1-score increased from 90.8% to 97.0%, and the overall accuracy (OA) increased from 91.9% to 96.9%.
ABSTRACTVegetation segmentation via point cloud data can provide important information for urban planning and environmental protection. The point cloud dataset is obtained using light detection and ranging (LiDAR) or RGB-D images. Oblique photogrammetry has received little attention as another important source of point cloud data. We present a pointwise annotated oblique photogrammetry point-cloud dataset that contains rich RGB information, texture, and structural features. This dataset contains five regions of Bengbu, China, with more than twenty thousand samples in this paper. Obviously, previous indoor point cloud semantic segmentation models are no longer applicable to oblique photogrammetry point clouds. A random sampling point transformer (RSPT) network is proposed to enhance vegetation segmentation accuracy. The RSPT model offers both efficient and lightweight architecture. In RSPT, random point sampling is utilized to downsample point clouds, and a local feature aggregation module based on self-attention is designed to extract additional representation features. The network also incorporated residual and dense connections (ResiDense) to capture both local and comprehensive features. Compared to state-of-the-art models, RSPT achieves notable improvements. The intersection over union (IoU) metric increased from 96.0% to 96.5%, the F1-score increased from 90.8% to 97.0%, and the overall accuracy (OA) increased from 91.9% to 96.9%.
Author Sun, Zhangyu
Kang, Ruihong
Hu, Hong
Wu, Yanlan
Wang, Baoguo
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Snippet Vegetation segmentation via point cloud data can provide important information for urban planning and environmental protection. The point cloud dataset is...
ABSTRACTVegetation segmentation via point cloud data can provide important information for urban planning and environmental protection. The point cloud dataset...
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SubjectTerms Accuracy
Aggregation
China
data collection
Datasets
Environmental protection
Image processing
Image segmentation
Lidar
Oblique photogrammetry
Photogrammetry
point cloud
Random sampling
RSPT
self-attention
Semantic segmentation
Statistical sampling
texture
Urban planning
Vegetation
vegetation segmentation
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Title Vegetation segmentation using oblique photogrammetry point clouds based on RSPT network
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