Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Design Example: Alignment of a Road Line Using GIS01:17

Design Example: Alignment of a Road Line Using GIS

38
The alignment of a road line using Geographic Information Systems (GIS) is a critical process in civil engineering, combining advanced technology with practical decision-making. This methodology begins with the collection of geospatial data, including information on land cover, geomorphology, drainage patterns, slope, and contour details. Such data is typically acquired through satellite imagery and GIS tools, offering a comprehensive understanding of the terrain.Once the data is gathered, it...
38
Vision01:24

Vision

52.9K
Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
52.9K
Sight Distance in a Vertical Curve01:29

Sight Distance in a Vertical Curve

33
Sight distance on vertical curves is critical in roadway design. It ensures drivers can see far enough ahead to identify and respond to hazards effectively. This directly impacts safety, driver comfort, and the overall efficiency of the transportation network.Vertical curves are classified into crest and sag curves based on their geometry. For crest curves, sight distance is determined by the line of sight between a driver's eye and a small object on the road's surface. Design parameters for...
33
Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

545
Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
545
  1. Home
  3. Research Domains
  5. Built Environment And Design
  7. Urban And Regional Planning
  9. Urban Informatics
  11. Enhancing The Visual Environment Of Urban Coastal Roads Through Deep Learning Analysis Of Street-view Images: A Perspective Of Aesthetic And Distinctiveness

Enhancing the visual environment of urban coastal roads through deep learning analysis of street-view images: A perspective of aesthetic and distinctiveness

Yu Zhang1, Xing Xiong1, Shanrui Yang1

  • 1Department of Landscape Architecture, Nanjing Agricultural University, Nanjing, China.

Plos One
|January 14, 2025

Related Experiment Videos

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

463
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

8.9K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.7K

View abstract on PubMed

Summary
This summary is machine-generated.

Coastal openness significantly impacts urban waterfront perception, while high green visuals may reduce distinctiveness. A random forest model accurately predicts human perception, guiding landscape improvements for over 60% of urban coastal roads.

Area of Science:

  • Urban planning
  • Environmental psychology
  • Geospatial analysis

Background:

  • Urban waterfronts are vital public spaces influencing city environments.
  • Understanding human perception of these areas is key for effective urban design.
  • Integrating visual landscape data with human perception is crucial for enhancing urban aesthetics.

Purpose of the Study:

  • To investigate the relationship between visual landscape features and human perception of urban waterfronts.
  • To develop predictive models for human perception along urban coastal roads.
  • To classify urban coastal roads based on aesthetic and distinctiveness perceptions for targeted improvements.

Main Methods:

  • Utilized deep learning and human perception data from street view images.
  • Employed linear regression and random forest models for perception prediction.

Related Experiment Videos

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

463
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

8.9K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.7K
  • Classified urban coastal roads in Xiamen based on aesthetic and distinctiveness perception.

    • Main Results:

    • Coastal openness was the most influential factor on human perception.
    • A high green visual index negatively impacted distinctiveness perception.
    • Random forest model achieved 87% (aesthetic) and 77% (distinctiveness) prediction accuracy.
    • 60.6% of road sections showed low perception, indicating improvement potential.
    • 10.5% of sections had both low aesthetic and low distinctiveness perception.

    Conclusions:

    • Human perception of urban coastal roads is strongly linked to visual landscape features like openness and greenery.
    • Predictive models, particularly random forest, offer effective tools for assessing and enhancing urban coastal road landscapes.
    • Findings provide evidence-based recommendations for improving the visual quality and human experience of urban waterfronts.