Deciphering Motorists’ Perceptions of Scenic Road Visual Landscapes: Integrating Binocular Simulation and Image Segmentation

Zhaocheng Bai, Rui Ji and Jun Qi ()
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Zhaocheng Bai: College of Landscape Architecture and Horticulture, Southwest Forestry University, Kunming 650224, China
Rui Ji: College of Architecture and Urban Planning, Tongji University, Shanghai 200092, China
Jun Qi: College of Landscape Architecture and Horticulture, Southwest Forestry University, Kunming 650224, China

Land, 2024, vol. 13, issue 9, 1-21

Abstract: Traditional scenic road visual landscape assessment methods struggle to quantify drivers’ subjective visual perceptions. This study aims to develop a new method to decipher Scenic Road Visual Landscape Evaluation (SRVLE) of motorists’ visual field, reconciling the longstanding subjectivity–objectivity dichotomy in landscape quality research. By adopting binocular visual simulation and image segmentation, this paper conceptualizes a novel “non-scale semantic differential approach” to quantify landscape qualities across the dimensions of naturalness–artificiality (NA), diversity–coherence (DC), and openness–deepness (OD), constructing a three-dimensional visual landscape quality evaluation system. Taking the Nujiang Beautiful Road in Yunnan as a case study, the results show the following: (1) The three indicators reveal the scenic road’s distinctive visual landscape characteristics, marked by high naturalness, coherence, and relative openness. (2) SRVLE is found to vary between the two driving directions and different sections. (3) The three-dimensional evaluation cube intuitively displays the comprehensive characteristics of landscape quality, providing a basis for scenic road planning. This method offers a new approach to resolving the subjective–objective divide in SRVLE and can assist road administrations in enhancing policy planning, construction, and management, thereby promoting the high-quality development of scenic roads.

Keywords: scenic road; visual landscape evaluation; field of view; image segmentation (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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