Automatic extraction of urban road boundaries using diverse LBP features

Liu, R.S.; Ma, Xudong; Lu, Xiaotian; Wang, Minghui; Wang, Peng

doi:10.11834/jrs.20209228

Cited by 3 publications

(2 citation statements)

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“…The midpoint of the line connecting the head and tail of the track points is chosen as the base point for rotation, while maintaining the line's parallelism to the horizontal axis of the grid coordinates. This coordinate rotation guarantees the utmost alignment between the grid division and the road boundary's orientation [9] , facilitating the acquisition of data points that preserve the linear attributes of the road surface. Subsequently, a planar grid projection is executed in order to generate a planar virtual grid that will be used for subsequent grid thinning.…”

Section: Pavement Point Grid Thinningmentioning

confidence: 99%

A 3D modelling method for road based on vehicle laser point cloud

Hu,

Liu,

Xie

et al. 2023

International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023)

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To address the challenges pertaining to the sluggish 3D reconstruction of the road surface, excessive workload, and limited adaptability, this study undertook the feature extraction of road point cloud data exhibiting diverse linear shapes, leveraging vehicle-mounted laser point cloud data. Additionally, a novel approach was devised to automatically construct the road triangulation network, thereby achieving a high-precision digital restoration of the road surface. Initially, the data attributes of the laser point cloud obtained from road vehicles are considered, and a data preprocessing technique is devised using the Alpha Shapes algorithm and the point cloud grid thinning algorithm. This method aims to retain crucial information pertaining to the elevation of the road surface, linear boundaries of the road surface, and skeletal features of the road surface. Subsequently, a technique is devised for constructing a Delaunay triangulation network using ordered point cloud data, based on the boundary points and feature points of the road surface. Additionally, an investigation is conducted on a method for constructing a road surface triangulation network with road boundary constraints, enabling the identification and removal of triangles located outside the road surface, thereby achieving precise restoration of the road surface. Ultimately, the assessment of the generated 3D pavement model's quality validates the method's feasibility and accuracy.

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Section: Pavement Point Grid Thinningmentioning

confidence: 99%

A 3D modelling method for road based on vehicle laser point cloud

Hu,

Liu,

Xie

et al. 2023

International Conference on Remote Sensing, Mapping, and Geographic Systems (RSMG 2023)

Self Cite

View full text Add to dashboard Cite

show abstract

“…Complex image backgrounds and similarities between roads and certain objects in the image: The presence of cluttered backgrounds in the images, along with high similarity between road features and other objects, poses significant challenges in road extraction tasks. Road extraction methods can be divided into two categories: traditional methods [12] and methods developed based on various deep learning frameworks [13]. Traditional methods heavily rely on manually designed features, thereby making it challenging to accurately identify road shape features in images [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Global–Local Information Fusion Network for Road Extraction: Bridging the Gap in Accurate Road Segmentation in China

Wang,

Cai,

et al. 2023

Remote Sensing

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Road extraction is crucial in urban planning, rescue operations, and military applications. Compared to traditional methods, using deep learning for road extraction from remote sensing images has demonstrated unique advantages. However, previous convolutional neural networks (CNN)-based road extraction methods have had limited receptivity and failed to effectively capture long-distance road features. On the other hand, transformer-based methods have good global information-capturing capabilities, but face challenges in extracting road edge information. Additionally, existing excellent road extraction methods lack validation for the Chinese region. To address these issues, this paper proposes a novel road extraction model called the global–local information fusion network (GLNet). In this model, the global information extraction (GIE) module effectively integrates global contextual relationships, the local information extraction (LIE) module accurately captures road edge information, and the information fusion (IF) module combines the output features from both global and local branches to generate the final extraction results. Further, a series of experiments on two different Chinese road datasets with geographic robustness demonstrate that our model outperforms the state-of-the-art deep learning models for road extraction tasks in China. On the CHN6-CUG dataset, the overall accuracy (OA) and intersection over union (IoU) reach 97.49% and 63.27%, respectively, while on the RDCME dataset, OA and IoU reach 98.73% and 84.97%, respectively. These research results hold significant implications for road traffic, humanitarian rescue, and environmental monitoring, particularly in the context of the Chinese region.

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A Pixel Texture Index Algorithm and Its Application

Sun,

Sun

2024

photogramm eng remote sensing

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Image segmentation is essential for object-oriented analysis, and classification is a critical parameter influencing analysis accuracy. However, image classification and segmentation based on spectral features are easily perturbed by the high-frequency information of a high spatial resolution remotely sensed (HSRRS) image, degrading its classification and segmentation quality. This article first presents a pixel texture index (PTI) by describing the texture and edge in a local area surrounding a pixel. Indeed.. The experimental results highlight that the HSRRS image classification and segmentation quality can be effectively improved by combining it with the PTI image. Indeed, the overall accuracy improved from 7% to 14%, and the kappa can be increased from 11% to 24%, respectively.

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Automatic extraction of urban road boundaries using diverse LBP features

Abstract: DeepLabv3+语义分割模型的济南市防尘绿网提取及时空变化分析Urban green plastic cover extraction and spatial pattern changes in Jinan city based on DeepLabv3+ semantic segmentation model

Cited by 3 publications

References 0 publications

A 3D modelling method for road based on vehicle laser point cloud

A 3D modelling method for road based on vehicle laser point cloud

Global–Local Information Fusion Network for Road Extraction: Bridging the Gap in Accurate Road Segmentation in China

A Pixel Texture Index Algorithm and Its Application

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