Work

Voting-Based Intervention Planning Using AI-Generated Images

Year: 2025

Type: article

Abstract: The continuous evolution of artificial intelligence and advanced algorithms capable of generating information from simplified input creates new opportunities for several scientific fields. Currently, the applicability of such technologies is limited to art and medical domains, but it can be applied to engineering domains to help the architects and urban planners design environmentally friendly solutions by proposing several alternatives in a short time. This work utilizes the image-inpainting algorithm for suggesting several alternative solutions to four European cities. In addition, this work suggests the utilization of a voting-based framework for finding the most preferred solution for each case study. The voting-based framework involves the participation of citizens and, as a result, decentralizes and democratizes the urban planning process. Finally, this research indicates the importance of deploying generative models in engineering applications by proving that generative AI models are capable of supporting the architects and urban planners in urban planning procedures.

Source: IEEE Computer Graphics and Applications

DOI: 10.1109/mcg.2025.3553620

Authors: Ioannis Kavouras, Ioannis Rallis, Emmanuel Sardis, Anastasios Doulamis, Nikolaos Doulamis

Institution: National Technical University of Athens

Language: English

Topic: Generative Adversarial Networks and Image Synthesis

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Sustainable cities and communities

Open Access status: hybrid

Estimated APC paid: $2,995