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

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Published on: December 15, 2023

FaceLift: a transparent deep learning framework to beautify urban scenes.

Sagar Joglekar^1,2, Daniele Quercia^1,2, Miriam Redi²

¹King's College, London, UK.

Royal Society Open Science

|March 29, 2020

Summary

This study introduces FaceLift, a deep learning framework that beautifies urban scenes and explains design elements. The framework successfully enhances urban spaces based on key metrics like walkability and green spaces.

Keywords:

deep learning explainable models generative models urban beauty urban design

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Area of Science:

Computer Vision
Urban Design
Artificial Intelligence

Background:

Deep learning models accurately predict urban beauty but lack actionable design insights.
Existing 'black-box' models fail to explain the reasoning behind aesthetic judgments in urban environments.
There is a need for AI that can both recreate and explain urban beauty to aid urban planning.

Purpose of the Study:

To develop a deep learning framework (FaceLift) capable of beautifying urban scenes and identifying contributing elements.
To create novel quantitative metrics for evaluating AI-driven urban design improvements.
To assess the framework's effectiveness in aligning with established urban planning principles.

Main Methods:

Proposed FaceLift, a deep learning framework for urban scene beautification and element explanation.
Developed new quantitative metrics based on urban planning literature: walkability, green spaces, openness, landmarks, and visual complexity.
Conducted a 20-participant expert survey to evaluate the framework's impact on urban design and citizen participation.

Main Results:

FaceLift successfully beautified urban scenes (Google Street Views).
Beautified scenes demonstrated improvements across all five key urban planning metrics.
Expert survey confirmed FaceLift's effectiveness in enhancing urban design and promoting citizen participation.

Conclusions:

FaceLift offers a novel approach to AI-assisted urban design, moving beyond prediction to recreation and explanation.
The framework aligns AI-driven enhancements with established principles of great urban spaces.
Future advancements in this technology hold significant potential for supporting architects and planners in creating beloved urban environments.