Visual Feature Evaluation of Shenyang Greenway Landscape Based on Deep Learning

Pan, Tianyang; Min, Jiang

doi:10.1155/2022/8730399

Cited by 3 publications

(3 citation statements)

References 25 publications

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“…At the same time, good visual perception can further promote people’s emotional recovery and reduce negative emotions. Therefore, some scholars have proposed relevant visual-optimization design strategies for coastal buildings based on visual perception ( Hu and Chen, 2020 ) or analyzed the visual characteristics of greenways and proposed corresponding planning and designs for greenway landscapes ( Pan and Jiang, 2022 ). At the same time, existing research has shown that visual perception plays a certain role in emotional recovery.…”

Section: Introductionmentioning

confidence: 99%

Tree–shrub–grass composite woodland better facilitates emotional recovery in college students emotion better than other plant communities

Fu,

Gao,

Zhang

et al. 2024

Front. Psychol.

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Previous research has indicated that natural landscapes exhibit a greater capacity for ameliorating negative emotional states in individuals when compared to urban landscapes. Nevertheless, significant scientific inquiries, such as the uniformity of the rejuvenating effect across distinct categories of natural landscapes on college students and the choice of the optimal plant community for achieving the most potent restorative effect, remain unexplored. This study aimed to address these questions by selecting four plant communities (single-layer grassland, single-layer woodland, tree-grass composite woodland, tree-shrub-grass composite woodland) and using an electroencephalography method to capture the neuroelectric activity of the participants in combination with the Positive and Negative Affect Schedule score to explore the effects of plant community types on emotional recovery. The results showed that all four plant communities significantly increased positive emotions and significantly reduced negative emotions. There was no significant difference in the recovery effect of positive emotions among the four plant community types, but there was a significant difference in the recovery effect of negative emotions. The effect of tree-shrub-grass composite woodland on the negative emotion recovery effect is the best; the EEG results found that the alpha wave amplitude induced by the tree-shrub-grass composite woodland was significantly higher than that of the other three groups of plant communities, and the EEG and behavioral results were consistent. The results show that the tree-shrub-grass composite woodland has the best restoration effect and has stronger planning and design significance.

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Section: Introductionmentioning

confidence: 99%

Tree–shrub–grass composite woodland better facilitates emotional recovery in college students emotion better than other plant communities

Fu,

Gao,

Zhang

et al. 2024

Front. Psychol.

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“…Feng et al (2022) integrated multi-source urban data and deep learning algorithms, taking the urban greenway planning research on both banks of Suzhou River in Shanghai as an example, to study human-oriented urban design planning issues, and provide strong support for human-oriented urban design practice [10]. Pan and Jiang (2022) constructed an evaluation index system for the suitability of urban greenway layout under the background of park city, and obtained the road with the highest suitability score through big data analysis. The final result of urban greenway layout is obtained through optimal path analysis and correction [11].…”

Section: Introductionmentioning

confidence: 99%

“…Pan and Jiang (2022) constructed an evaluation index system for the suitability of urban greenway layout under the background of park city, and obtained the road with the highest suitability score through big data analysis. The final result of urban greenway layout is obtained through optimal path analysis and correction [11]. He et al…”

Section: Introductionmentioning

confidence: 99%

Innovative Design of Environmental Visual Art Based on Edge Cloud Computing Technology under the Background of Artificial Intelligence

Han

Yao

2022

Preprint

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In the traditional artificial city planning, the greenway research generally takes the greenway layout or environmental effects as a single evaluation criterion. This work aims to solve the problem of weak commonality and difference of greenway landscape vision in the construction of urban style features. First, an image super-resolution reconstruction technology is proposed based on the Convolutional Neural Network to achieve precise processing of the urban greenway layout at a high level. Secondly, a one-time batch processing technology of images based on edge-cloud collaborative deep learning semantic segmentation of edge-cloud computing technology is proposed, which can quickly find out the problematic areas in greenway planning. Finally, the data set obtained by image processing is expressed by mapping software to analyze the commonness and difference of urban greenway landscape visual elements characteristics and composition diversity. The results show the spatial distribution sequence characteristics of different types of greenways and evaluate the relationship between the same type of greenway landscape visual services and their accessibility. The experimental results show that: (1) the CNN-based image super-resolution reconstruction technology designed is superior to the traditional technology in image processing and can more accurately identify the information in the image; (2) The image batch processing technology of edge-cloud collaborative deep learning semantic segmentation based on edge cloud computing technology can greatly improve the efficiency of image information processing, and has broader application prospects in image batch processing. This research aims to provide technical support for the upgrading and transformation of artificial intelligence technology in technology enterprises and reduce the adverse impact on urban construction caused by blind and one-sided urban environmental planning. Some method innovation and reference are provided for environmental visual art design innovation.

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Retracted: Visual Feature Evaluation of Shenyang Greenway Landscape Based on Deep Learning

2023

Security and Communication Networks

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Visual Feature Evaluation of Shenyang Greenway Landscape Based on Deep Learning

Cited by 3 publications

References 25 publications

Tree–shrub–grass composite woodland better facilitates emotional recovery in college students emotion better than other plant communities

Tree–shrub–grass composite woodland better facilitates emotional recovery in college students emotion better than other plant communities

Innovative Design of Environmental Visual Art Based on Edge Cloud Computing Technology under the Background of Artificial Intelligence

Retracted: Visual Feature Evaluation of Shenyang Greenway Landscape Based on Deep Learning

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