Meta-Connectivity in Urban Morphology: A Deep Generative Approach for Integrating Human–Wildlife Landscape Connectivity in Urban Design

Abstract

Traditional urban design often overlooks the synchronisation of human and ecological connectivities, typically favouring corridors for ecological continuity. Our study challenges this convention by introducing a computational design approach, meta-connectivity, leveraging the deep generative models performing cross-domain translation to integrate human–wildlife landscape connectivity in urban morphology amidst the planetary urbanisation. Utilising chained Pix2Pix models, our research illustrates a novel meta-connectivity design reasoning framework, combining landscape connectivity modelling with conditional reasoning based on deep generative models. This framework enables the adjustment of both human and wildlife landscape connectivities based on their correlative patterns in one single design process, guiding the rematerialisation of urban landscapes without the need for explicit prior ecological or urban data. Our empirical study in East London demonstrated the framework’s efficacy in suggesting wildlife connectivity adjustments based on human connectivity metrics. The results demonstrate the feasibility of creating an innovative urban form in which the land cover guided by the connectivity gradients replaces the corridors based on simple geometries. This research thus presents a methodology shift in urban design, proposing a symbiotic approach to integrating disparate yet interrelated landscape connectivities within urban contexts.

Keywords

• urban design
• deep generative models
• cross-domain translation
• landscape connectivity
• progressive reasoning
• urban ecology