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Modeling the Visual Landscape: A Review on Approaches, Methods and Techniques.

Loukas-Moysis Misthos1,2, Vassilios Krassanakis1, Nikolaos Merlemis1

  • 1Department of Surveying and Geoinformatics Engineering, University of West Attica, GR-12243 Athens, Greece.

Sensors (Basel, Switzerland)
|October 14, 2023
PubMed
Summary

This review models human landscape perception and evaluation using visual data. It integrates egocentric and exocentric views with sensor technologies for better landscape management and preference prediction.

Keywords:
EEGGISegocentric/exocentric landscape perspectiveseye trackingfMRIlandscape characterizationlandscape evaluationlandscape perceptionlandscape quantification and modelingsensor-based experimental techniques

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Area of Science:

  • Environmental Psychology
  • Human-Computer Interaction
  • Geospatial Science

Background:

  • Human visual perception is key to understanding environmental stimuli.
  • Landscape experience involves both perception and cognitive evaluation, often unpredictably.
  • Landscapes are viewed from egocentric (human) and exocentric (bird's eye) perspectives.

Purpose of the Study:

  • To systematically present methods for modeling and quantifying human landscape perception and evaluation.
  • To integrate egocentric and exocentric perspectives in landscape analysis.
  • To suggest integrative approaches for understanding landscape experience and management.

Main Methods:

  • Review of existing traditions, sensor-based experimental methods (eye tracking, fMRI, EEG), and metrics.
  • Analysis of two main research directions: perspective transfer and perception-preference connection.
  • Delineation of activities related to landscape experience and management with suitable stimuli, sensors, and metrics.

Main Results:

  • Identified two primary research directions: perspective transfer and anticipating visual perception.
  • Highlighted the growing body of research in landscape perception and evaluation.
  • Emphasized the need for integrative, interdisciplinary approaches.

Conclusions:

  • Integrative approaches can advance understanding of how visual landscapes influence perception and preferences.
  • Sensor-based technologies offer advanced tools for landscape analysis.
  • Evidence-based, socially just landscape management can be supported through rigorous, technology-driven frameworks.