Search

The research is based on the hypothesis that integrating site-specific and global data into the design process requires a methodological design approach, which connects local to global systems and extends the application of available predefined algorithmic scripts and singular solutions. These tools allow the designer to apprehend and simulate possible future scenarios with unparalleled precision and speed. Computational design thinking will help us master increasingly complex design challenges as well as build a profound theoretical knowledge base to meaningfully integrate current and future technologies. After re-evaluating the principles of the computational pioneers, computationally driven methods for pressing urban challenges through data-informed design speculations are discussed. Cutting-edge design speculations aim to open up new immersive design simulation and participatory processes in environmental design and urban development and give sustainable answers to societal and environmental challenges, ultimately shaping our future world.

Deep Learning (DL) has recently gained widespread attention in the automation of urban layout processes. This study proposes a rule-based and Generative Adversarial Network (GAN) workflow to automatically select and label urban datasets to train customized GAN models for the generation of urban layout proposals. The developed workflow automatically collects and labels urban typology samples from open-source maps. Furthermore, it controls the results of the GAN process with labels and provides real-time urban layout suggestions based on a co-design process. The conducted case study shows that the average value of the GAN results, trained from an automatically generated dataset, meets the site's requirements. The developed co-design strategy allows the architect to control the GAN process and perform iterations on urban layouts. The research addresses the research gap in GAN applications in the field of urban design and planning. Many studies have demonstrated that training the (GAN) model by labeling enables machines to learn urban morphological features and urban layout logic. However, two research gaps remain: (1) The manual filtering of GAN urban sample datasets to fit site-specific design requirements is very time-consuming. (2) Without a suitable data labeling method, it is difficult to manage the GAN process in such a manner to facilitate the meeting of overriding design requirements. publishedVersion

We investigate deep neural networks in recognizing playful physical activity opportunities of the urban environment. Using transfer learning with a pre-trained Faster R-CNN network, we are able to train a parkour training spot detector with only a few thousand street level photographs. We utilize a simple and efficient annotation scheme that only required a few days of annotation work by parkour hobbyists, and should be easily applicable in other contexts, e.g. skateboarding. The technology is tested through parkour spot exploration and visualization experiments. To inform and motivate the technology development, we also conducted an interview study about what makes an interesting parkour spot and how parkour hobbyists find spots. Our work should be valuable for researchers and practitioners of fields like urban design and exercise video games, e.g., by providing data for a location-based game akin to Pokémon Go, but with parkour-themed gameplay and challenges.

This paper explores and reflects on an integrative computational design thinking approach, which requires the melding of computation, design and theory as a conceptual framework, to be implemented in architectural education. Until now, digital design education is typically based on the introduction of digital tools and plugins at university courses and the subsequent application of these tools to design tasks of limited architectural complexity. At this time, technological advancement has not been matched by a comparable advancement in computational design thinking. The paper describes in detail a novel conceptual framework for course setup that illustrates the using of computational design as a manner of thinking in patterns of interaction across various scales, reaching from building design to regional planning. This approach was subsequently tested in a series of master-level studios, the results of which will be presented as case studies in this paper.

Please cite this article as: Fricker, P. (2019). Virtual Reality for Immersive Data Interaction. Landscape Architecture Frontiers, 7(2), 153-159. https://doi:10.15302/J-LAF-20190216 In a time shaped by technological developments dissolving the boundaries between the real and the virtual world, we are challenged to newly define the potentials of virtual and mixed reality in the field of landscape architecture. Critical analyses of the primary application areas of these technologies show that the current focus mostly lies on the optimization of 3D visualization and navigation in virtual space. Within professional practice, mixed reality tools are increasingly being used to test and communicate design decisions, for marketing purposes, and more often, within the smart building industry as well. Thus far, the incorporation of immersive environments in landscape architecture is lacking research on human-centered data interaction and the perception of space. At Aalto University, Finland, the team of Pia Fricker, Professor of Practice for Computational Methodologies in Landscape Architecture and Urbanism, researches new immersive co-design methodologies to introduce new meaningful trajectories for participatory processes. Mixed reality applications are thus extended beyond common and conventional uses to help create multidisciplinary immersive interactive spaces using data informed processes. The research and teaching results showcased in the article address international discourse on the larger theme of “Digital Humanism — Big Data and Human- Centered Design.”

In this era of progressive digitalization, the precise choice of tools from the area of information technology poses a great challenge. Through simplified access to highly specialized software and hardware, one can make out a trend towards highly complex and often no longer controllable and realizable forms. Which methodologies and workflows within landscape architecture are suitable in order to react to the specific requirements of a place and the growing challenges of the discipline? Global urban questions and tendencies towards urban sprawl require strategies for solutions that integrate geographical, ecological, sociological, and infrastructural factors into planning. The integration of these parameters within design, however, often requires training specifically aimed at honing the skills needed to be able to sift through the existing data overload and find the useful data within.

Driven by technological advances, growing amounts of available data, and an emergent need for participatory processes, landscape architecture is witnessing a moment of disruption whereby formerly separated areas of operation become increasingly connected. While distinctions between various aspects of the design process are diminishing, a need for a novel, more inclusive toolset arises. The ‘tangible table’ paradigm is an attempt at combining intuitive ways of physical modelling with data-driven design strategies and the interactive simulation of naturally occurring phenomena. Despite its existence for more than 20 years, tangible tables have mainly focused on very specific workflows and therefore have not found wider adoption in landscape architectural practice or education. We list the limitations of previous implementations and introduce a novel software solution aimed at popularizing tangible table setups. Our software is embedded in a widespread visual programming environment, which allows for straightforward augmentation of physical models with computational design tech-niques. Using a week-long PhD course as a case study, we demonstrate the usefulness of the proposed software and its potential applications to solving various landscape architectural challenges through increased emphasis on participatory processes.