By studying how historical green systems were designed and managed, strategies and methods for developing novel urban green systems can be gained. However, this potential often remains untapped due to the high complexity of historical approaches. New methods and tools in the areas of 3D scanning, data-driven modelling and simulation, computer-aided design, information modelling, knowledge engineering, machine learning and decision support systems can serve to tackle this complexity. In this paper we review the related state of the art and conceptually outline a digital workflow to achieve this aim. We conclude with discussing how this approach can change the role of landscape designers in the future.
Autor / Author: | Ludwig, Ferdinand; Hensel, Michael; Rötzer, Thomas; Ahmeti, Albin; Chen, Xi; Erdal, Halil Ibrahim; Reischel, Astrid; Shu, Qiguan; Tyc, Jakub Marcin; Yazdi, Hadi |
Institution / Institution: | Technical University of Munich/Germany; Vienna University of Technology, Vienna/Austria; Technical University of Munich/Germany; Vienna University of Technology, Vienna/Austria; Technical University of Munich/Germany; Technical University of Munich/Germany; Technical University of Munich/Germany; Technical University of Munich/Germany; Vienna University of Technology, Vienna/Austria; Technical University of Munich/Germany |
Seitenzahl / Pages: | 13 |
Sprache / Language: | Englisch |
Veröffentlichung / Publication: | JoDLA – Journal of Digital Landscape Architecture, 9-2024 |
Tagung / Conference: | Digital Landscape Architecture 2024 – New Trajectories in Computational Urban Landscapes and Ecology |
Veranstaltungsort, -datum / Venue, Date: | Vienna University of Technology, Austria 05-06-24 - 07-06-24 |
Schlüsselwörter (de): | |
Keywords (en): | Urban trees, computational design, target-driven design, data-driven design, knowledge graph |
Paper review type: | Full Paper Review |
DOI: | doi:10.14627/537752030 |
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