Shades of Green: AI-Driven Investigation on Spatial Justice in Cleveland

The Shades of Green exhibition and research project examines/demonstrates how machine learning, generative AI, computer vision, and principles of human-computer interaction can address pressing questions of environmental justice in Cleveland. By integrating computational tools with urban analysis, the project highlights the potential of AI-driven approaches to reimagine more equitable urban environments.

Focusing on two contrasting neighborhoods in Northeast Ohio—East Cleveland and Shaker Heights—the research leverages advanced AI techniques and innovative data collection methods to analyze urban green spaces. Using AI for visual data analysis, the project uncovers patterns of environmental inequality by quantifying key indicators such as tree canopy coverage, grass density, and the visual quality of vegetation.

In parallel, the research and exhibition explore speculative worldbuilding, interactive digital art, and custom-made real-time generative AI as tools for envisioning alternative urban futures. These interactive frameworks offer a lens to imagine more equitable urban environments, using data visualization and public art to provoke critical discussions about how equitable green infrastructures can address pressing environmental and social challenges.

By merging computational techniques with urban design and planning, interactive data visualization with data analysis, and analytical urban studies with speculative design,

Shades of Green underscores the importance of urban vegetation—not only in terms of its quantity but also its aesthetic and spatial characteristics—as essential components of environmental equity.