Deep Tuning of Urban Ecology
As revealed in Planetary Urbanisation, wilderness spaces consistently decay due to the unfettered worldwide spread of the urban fabric. If the end of wildness is inevitable in the planetary urbanisation, how can we recover the biodiversity and live with local wildlife in the perpetual urbanisation process? The contemporary use of ecological corridor has raised serious concerns in environmental justice; its design heavily relies on well-built, explicit knowledge and always confronts the ideology dilemmas with how to position humans in the making of urban ecology. This paper proposes an integrated AI reasoning framework for the anthropocentric design searching the latent spatial pattern of urban ecology in the Anthropocene epoch. Our proposal combines the connectivity methodology and conditional generative adversarial network (cGAN) to algorithmically enable the cross-domain reasoning between wildness and humanity and to create design knowledge without determined rules and specific data of wildlife. The proposed framework consists of three components: 1) connectivity modelling, 2) progressive reasoning and 3) parametric adaptation. It is tested with a design exercise on a 1km*1km site in East London: The human connectivity and wildlife connectivity of one hundred selected reference locations are modelled to train a CycleGAN to suggest the wildlife connectivity based on the human connectivity of the site, and another CycleGAN turns it into a materiality reference. With the reference, parametric prototypes are adopted to adapt the extant urban landscape to the suggested condition in a parametric approach. The study emphasises on how the framework leads to an Anthropocentric ‘sweet spot’ of urban ecology tuned under the limited availability of local wildlife data instead of developing a design work fully. The result suggests the creation of the discrete urban ecological field can be led by an eco-intensity-graded, mass?customisable framework.