BoundaryNet: Extraction and Completion of Road Boundaries With Deep Learning Using Mobile Laser Scanning Point Clouds and Satellite Imagery

Ma, Lingfei; Li, Ying; Li, Jonathan; Marcato, José; Gonçalves, Wesley Nunes; Chapman, Michael A.

doi:10.1109/tits.2021.3055366

Cited by 18 publications

(11 citation statements)

References 55 publications

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“…Using the correlation coefficient method, through the correlation analysis of the image road information before and after the road damage, the geometric information and attribute information of the road damage are obtained [5]. Ma et al proposed an interpolation method to compare road extraction [6]. The difference method is to extract the road from the registered image of the same area, then perform the difference operation to obtain a difference change image, and then analyze, judge, and extract the change information.…”

Section: Literature Reviewmentioning

confidence: 99%

Application of Road Extraction from High-Resolution Remote Sensing Images in Tourism Navigation and GIS

Wang¹,

Liang

2022

Wireless Communications and Mobile Computing

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In order to quickly and accurately obtain and process road information and its damage information, to obtain better tourism navigation experience and accurate geographic information, this paper proposes a road extraction method based on high-resolution remote sensing images. Based on the known road width information and the analysis of the gray-scale characteristics of the road surface, the method takes the road center line and straight line in the image as the center line and then deduces the road edge line information, so as to extract the edge information. Then, through the image de redundancy information processing, the final obtained road width parameters are compared to verify the accuracy and reliability of the method. The experiment shows that the road information can be extracted accurately, and the extraction accuracy and efficiency have been significantly improved, reaching more than 90%, and can directly reflect the road edge line, median line, and width information. Conclusion. The road extraction method based on high-resolution remote sensing images can effectively and accurately extract road information, obtain better tourism navigation experience, and accurate geographic information.

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Section: Literature Reviewmentioning

confidence: 99%

Application of Road Extraction from High-Resolution Remote Sensing Images in Tourism Navigation and GIS

Wang¹,

Liang

2022

Wireless Communications and Mobile Computing

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“…The scale parameter is a coefficient number that controls the number of the dominant queries in our proposed Dominant-Transformer block. We set the value of the scale parameter to [1,3,5,7,9,10,30,50,90] and tested their performance. The evaluation indicators of different scale values are shown in TABLE V. The third column in the table is the value of U , the number of dominant queries.…”

Section: Effect Of the Scale Parameter Of Dominant Transformermentioning

confidence: 99%

UGRoadUpd: An Unchanged-Guided Historical Road Database Updating Framework Based on Bi-Temporal Remote Sensing Images

Zhou

Sui

Chen

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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Timely updated road networks are the basis for many real-world applications such as intelligent navigation and traffic management. Existing road updating methods based on remote sensing images learn from historical road databases to update roads. Road extraction models learned from historical images however, are not easily applied to a current image due to spectral differences; and only changed roads need updating. In this paper, an Unchanged-Guided Road Updating (UGRoad-Upd) framework is proposed to improve the quality of updated road networks by limiting the road updating range and learning from historical unchanged roads. The UGRoadUpd framework identifies road changes using a novel dual-task dominanttransformer-based neural network for road change detection (DT-RoadCDNet). DT-RoadCDNet executes road segmentation and change detection simultaneously, from bi-temporal remote sensing images. The Dominant-Transformer based Global Context Modeling module in DT-RoadCDNet globally models the contextual spatial structure for improved integrity in roads and road changes. Based on the discovery of road changes, an unchangedguided road update strategy updates the roads in changed areas by learning from the prior information provided by unchanged roads in a historical road database. Experiments on two newly annotated road change detection and update datasets confirms the effectiveness of our UGRoadUpd framework.

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“…It is a partially connected neural network structure. In order to realize the local correlation in the space of each layer, CNN forces the local connection of neurons in adjacent layers [17]. In other words, the input of each neuron in the latent m layer is obtained by connecting local units in the m − 1 layer, see Figure 2.…”

Section: Deep Learningmentioning

confidence: 99%

Feature Extraction and Classification of Music Content Based on Deep Learning

Shi¹,

2022

Advances in Multimedia

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To study the use of in-depth training in extracting and classifying the content of music samples, the work offers an algorithm for identifying and classifying musical genres based on a deep network of beliefs, enabling it to be used to extract and classify traditional Chinese musical instruments, and using real-world experiments to test its performance after training. The experimental results are as follows: the improved depth confidence network algorithm has the highest accuracy for music recognition and classification, which can reach 75.8%, higher than other traditional methods. The improved depth confidence network identifies and classifies Chinese traditional musical instruments through Softmax layer, and the accuracy is even as high as 99.2%; DBN is combined with Softmax neural network algorithm when only a few labeled samples in the training set are used for network fine-tuning, and the accuracy of the algorithm can still reach more than 90%, which can reduce the workload in the early stage. This study effectively solves the problem of too much workload and low accuracy in the process of music content recognition, classification, and extraction.

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BoundaryNet: Extraction and Completion of Road Boundaries With Deep Learning Using Mobile Laser Scanning Point Clouds and Satellite Imagery

Cited by 18 publications

References 55 publications

Application of Road Extraction from High-Resolution Remote Sensing Images in Tourism Navigation and GIS

Application of Road Extraction from High-Resolution Remote Sensing Images in Tourism Navigation and GIS

UGRoadUpd: An Unchanged-Guided Historical Road Database Updating Framework Based on Bi-Temporal Remote Sensing Images

Feature Extraction and Classification of Music Content Based on Deep Learning

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