With the advent of the modern transportation era, road design needs to emphasize landscape and recreational functions more than traditional safety functions. However, quantifying drivers’ subjective perceptions and the objective visual landscape characteristics that influence them remains challenging due to the subjectivity of landscape perception, and the limitation of data size and the complexity of environmental information capture. This paper aims to overcome these limitations, based on deep learning techniques to achieve high-precision automatic identification and rapid processing of spatial visual elements in the highway landscape. Based on supervised fully connected neural networks, the ELO scoring algorithm, and ArcGIS, it realizes the quantitative evaluation of subjective perception emotions and visual mapping representation. Furthermore, the paper integrates optimization algorithms with deep learning, employing a Whale Optimization Algorithm (WOA) enhanced XGBoost regression model to analyze the interplay between spatial features and subjective experiences. The model’s effectiveness is confirmed through comparative experiments and SHAP analysis, offering insights into the coupling of subjective and objective data. The research results provide theoretical methods and quantitative analysis tools for the digitalization and refinement of highway landscape design, and also help researchers and urban planners to understand the interaction between subjective perception and objective semantics, providing new ways and insights for highway landscape planning and design.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Quantitative Characterization of Highway Landscape Space Visual Perception Based on Deep Learning


    Contributors:

    Published in:

    Publication date :

    2024-12-01


    Size :

    7021785 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Quantitative Analysis Indices of Highway Landscape Design

    Zhou, H. Z. / Wei, D. X. / Yang, H. Z. | British Library Conference Proceedings | 2015



    The Impact of Character Differences of Highway Landscape on Aesthetic Perception

    Wang, Y. / Li, H. / Cui, P. et al. | British Library Conference Proceedings | 2007


    Visual Perception and Navigation of Security Robot based on Deep Learning

    Li, Xiuzhi / Guo, Kangkai / Jia, Tong et al. | British Library Conference Proceedings | 2020