Machine Learning for Land Use Scenarios and Urban Design

Podrasa, Daniel and Zeile, Peter and Neppl, Markus (2021) Machine Learning for Land Use Scenarios and Urban Design. CITIES 20.50 – Creating Habitats for the 3rd Millennium: Smart – Sustainable – Climate Neutral. Proceedings of REAL CORP 2021, 26th International Conference on Urban Development, Regional Planning and Information Society. pp. 489-498. ISSN 2521-3938

[img] Text (Machine Learning for Land Use Scenarios and Urban Design)
CORP2021_73.pdf - Published Version
Download (4MB)
Official URL: https://www.corp.at/

Abstract

Geographic Information Systems (GIS) are becoming a more common tool in the practice of urbanism and urban design. Usually, GIS is used to visualize geo-located data to gain inside into the urban fabric, to either plan interventions within it, restructure it, or extend it. One problem for a data-driven planning process with GIS is how to turn the gained data into knowledge to drive a project. This paper discusses the use of super- and unsupervised machine learning to develop land-use scenarios for a vacant site within the city parameters of Berlin. Unsupervised learning is used to find cluster which shares certain characteristics. This interpretation of the data helps to make more informed decisions. As an example, for supervised learning, a neural network was trained to develop land-use scenarios fully autonomously. Autonomously generated land-use scenarios are an essential step to bridge the gap between the analysis and the design phase of urban development and enable the use of artificial intelligence in the planning process.

Item Type: Article
Uncontrolled Keywords: Land-Use Scenarios, Urban Design, Neurnal Networks, Machine Learning, GIS
Subjects: H Social Sciences > HD Industries. Land use. Labor
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Depositing User: REAL CORP Administrator
Date Deposited: 27 Sep 2021 13:19
Last Modified: 17 Oct 2021 17:27
URI: http://repository.corp.at/id/eprint/777

Actions (login required)

View Item View Item
REAL CORP Repository is powered by EPrints 3 which is developed by the School of Electronics and Computer Science at the University of Southampton. More information and software credits.