31 Human-Centered AI for Adaptive Urban Design: A Multimodal Generative Framework for Sustainable and Inclusive Cities

Abstract

Rapid urbanization presents complex challenges, including environmental degradation, social inequity, and diminished citizen well-being. Traditional urban planning often struggles to adapt to dynamic urban environments and integrate diverse stakeholder needs, leading to static designs that fail to foster sustainable and inclusive communities. While Artificial Intelligence (AI) offers powerful tools for optimization, its application in urban design frequently overlooks human-centric values and participatory processes, resulting in solutions that are technically efficient but socially detached.

This paper introduces a novel Human-Centered AI (HCAI) framework designed for adaptive urban space design. Our approach integrates advanced multimodal data fusion techniques with generative design algorithms, underpinned by design thinking methodologies. This framework facilitates an iterative co-creation process, enabling urban planners and designers to collaboratively explore and refine complex design solutions.

The HCAI framework leverages diverse datasets, including real-time environmental sensor data, social media sentiment analysis, demographic information, and qualitative feedback from community engagement platforms. Utilizing Generative Adversarial Networks (GANs) and other generative models, the framework generates a multitude of design alternatives, which are then evaluated against human-centric metrics such as walkability, green space accessibility, noise reduction, and social interaction potential. A continuous feedback loop allows for refinement based on human input.

Our findings demonstrate that the HCAI framework significantly enhances the adaptability and inclusivity of urban designs. Through iterative co-creation, the framework achieves optimal solutions that not only meet functional requirements but also profoundly improve citizen well-being and environmental sustainability. The generated designs exhibit a higher degree of responsiveness to dynamic urban conditions and diverse community needs compared to conventional methods.