Automating green space design concepts and habitat quality indexing with an AI generative model

Landscape architects work on multidisciplinary projects that require effective design decision-making and communication methods. Habitat restoration, for example, involves diverse objectives from professionals and the public that are sometimes in opposition (ecological value vs aesthetic preference). Here we leverage machine learning applications to improve inclusive design collaborations with an AI model for presenting and quantifying conceptual landscape design scenarios. Our goal is to train a model for automating conceptual green space designs that are supported by a quantitative ‘restoration index’. Through generating multiple designs, conceptual scenarios can be re-generated in response to feedback that supports the initial stages of design.