Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
December 1, 2025GeoScapeOpen Access

Unveiling greenery and visual comfort: Integrating Green View Index and image segmentation in panoramic rural landscapes

View Full Paper
Ask AI
Bookmark
Share

Authors

MPMohammad Raditia PradanaDMDimyati MuhammadJSJarot Mulyo Semedi

Discussion

Loading...

Member takes

Overview

Observational modeling reveals visual comfort depends on greenery distribution and object composition in rural landscapes, indicating the value of visual diversity for rural planning.

Key Points

  • To examine the relationship between visual comfort, the Green View Index, and specific object compositions in rural panoramic landscapes.
  • Captured panoramic imagery and applied semantic segmentation models to identify and categorize dominant visual landscape elements.
  • Evaluated predictors of visual comfort using both linear regression and Random Forest machine learning models.
  • Random Forest modeling demonstrated superior explanatory power (R² = 0.60) compared to linear regression (R² = 0.37), reflecting nonlinear relationships.
  • Green View Index was the dominant determinant of visual comfort, with trees, mountains, and sky exerting positive effects, while walls exerted negative effects.
  • Plants placed in primary visual positions decreased visual comfort due to monotony, whereas plants in secondary or tertiary positions enhanced visual comfort by adding diversity.

Cite This Study

Pradana et al. (2025) studied this question.

synapsesocial.com/papers/6a0fffd44fb650da4ffecad4https://doi.org/10.2478/geosc-2025-0011
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Multi-Perspective Evaluation of Urban Green Views: Spatial and Street-View Data Integration in Sudirman Central Business District, Indonesia2025 · 3 citations
  2. 2A practical method for assessing greenery visibility in urban environments using a 360-degree camera2026
  3. 3Investigating Spatial Variation Characteristics and Influencing Factors of Urban Green View Index Based on Street View Imagery—A Case Study of Luoyang, China2025
  4. 4Spatial-temporal patterns and influencing factors of the Building Green View Index: A new approach for quantifying 3D urban greenery visibility2024 · 9 citations
  5. 5Street view versus remote sensing greenery – comparison of two exposure metrics across urban-rural settings2026 · 2 citations