Image-based Visibility Analysis Replacing Line-of-Sight Simulation: An Urban Landmark Perspective

• URL: http://arxiv.org/abs/2505.11809v1
• Date: Sat, 17 May 2025 03:41:45 GMT
• Title: Image-based Visibility Analysis Replacing Line-of-Sight Simulation: An Urban Landmark Perspective
• Authors: Zicheng Fan, Kunihiko Fujiwara, Pengyuan Liu, Fan Zhang, Filip Biljecki,
• Abstract summary: The study challenges the traditional LoS-based approaches by introducing a new, image-based visibility analysis method.<n>In the first case study, the method proves its reliability in detecting the visibility of six tall landmark constructions in global cities, with an overall accuracy of 87%.<n>In the second case, the proposed visibility graph uncovers the form and strength of connections for multiple landmarks along the River Thames in London.
• Score: 2.3315115235829342
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visibility analysis is one of the fundamental analytics methods in urban planning and landscape research, traditionally conducted through computational simulations based on the Line-of-Sight (LoS) principle. However, when assessing the visibility of named urban objects such as landmarks, geometric intersection alone fails to capture the contextual and perceptual dimensions of visibility as experienced in the real world. The study challenges the traditional LoS-based approaches by introducing a new, image-based visibility analysis method. Specifically, a Vision Language Model (VLM) is applied to detect the target object within a direction-zoomed Street View Image (SVI). Successful detection represents the object's visibility at the corresponding SVI location. Further, a heterogeneous visibility graph is constructed to address the complex interaction between observers and target objects. In the first case study, the method proves its reliability in detecting the visibility of six tall landmark constructions in global cities, with an overall accuracy of 87%. Furthermore, it reveals broader contextual differences when the landmarks are perceived and experienced. In the second case, the proposed visibility graph uncovers the form and strength of connections for multiple landmarks along the River Thames in London, as well as the places where these connections occur. Notably, bridges on the River Thames account for approximately 30% of total connections. Our method complements and enhances traditional LoS-based visibility analysis, and showcases the possibility of revealing the prevalent connection of any visual objects in the urban environment. It opens up new research perspectives for urban planning, heritage conservation, and computational social science.

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