DeepScope: HCI Platform for Generative Cityscape Visualization

Streetscape visualizations are necessary for the understanding and evaluation of urban design alternatives. Alongside blueprints and textual descriptions, these design aids can affect city-form, building-codes and regulations for decades to come. Yet despite major advancements in computer graphics, crafting high-quality streetscape visualizations is still a complex, lengthy and costly task, especially for real-time, multiparty design sessions. Here we present DeepScope, a generative, lightweight and real-time HCI platform for urban planning and cityscape visualization. DeepScope is composed of a Generative Neural Network (DCGAN) and a Tangible User Interface (TUI) designed for multi-participants urban design sessions and real-time feedback. In this paper we explore the design, development and deployment of the DeepScope platform, as well as discuss the potential implementation of DeepScope in urban design processes.

[1]  Hiroshi Ishii,et al.  Sensetable: a wireless object tracking platform for tangible user interfaces , 2001, CHI.

[2]  Sebastian Ramos,et al.  The Cityscapes Dataset for Semantic Urban Scene Understanding , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Léon Bottou,et al.  Wasserstein Generative Adversarial Networks , 2017, ICML.

[4]  Stefano Brusaporci,et al.  The Importance of Being Honest: Issues of Transparency in Digital Visualization of Architectural Heritage , 2017 .

[5]  Lawrence D. Jackel,et al.  Backpropagation Applied to Handwritten Zip Code Recognition , 1989, Neural Computation.

[6]  Kheir Al-Kodmany,et al.  Using visualization techniques for enhancing public participation in planning and design: Process, implementation, and evaluation , 1999 .

[7]  Hiroshi Ishii,et al.  Augmented urban planning workbench: overlaying drawings, physical models and digital simulation , 2002, Proceedings. International Symposium on Mixed and Augmented Reality.

[8]  Tom White,et al.  Generative Adversarial Networks: An Overview , 2017, IEEE Signal Processing Magazine.

[9]  Jie Yan.  An Evaluation of Current Applications of 3D Visualization Software in Landscape Architecture , 2016 .

[10]  Andrew A. Lovett,et al.  Using 3D visualization methods in landscape planning: An evaluation of options and practical issues , 2015 .

[11]  Alexei A. Efros,et al.  Image-to-Image Translation with Conditional Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Kevin Lynch,et al.  The Image of the City , 1960 .

[13]  Ariel Noyman.  POWERSTRUCTURES : the urban form of regulations , 2015 .

[14]  Daniel J. Wigdor,et al.  How Much Faster is Fast Enough?: User Perception of Latency & Latency Improvements in Direct and Indirect Touch , 2015, CHI.

[15]  Xiaohua Zhai,et al.  High-Fidelity Image Generation With Fewer Labels , 2019, ICML.

[16]  Jiwon Kim,et al.  Continual Learning with Deep Generative Replay , 2017, NIPS.

[17]  Wojciech Zaremba,et al.  Improved Techniques for Training GANs , 2016, NIPS.

[18]  Y Wang,et al.  Bringing Clay and Sand into Digital Design — Continuous Tangible user Interfaces , 2004 .

[19]  Stefano Brusaporci.  The Importance of Being Honest , 2017 .

[20]  Ivan E. Sutherland,et al.  Sketchpad a Man-Machine Graphical Communication System , 1899, Outstanding Dissertations in the Computer Sciences.

[21]  Ronan Doorley,et al.  What’s your MoCho? Real-time Mode Choice Prediction Using Discrete Choice Models and a HCI Platform , 2019 .

[22]  Ariel Noyman,et al.  CityScopeAR: Urban Design and Crowdsourced Engagement Platform , 2019, ArXiv.

[23]  Gerhard Schmitt,et al.  Citizen Design Science: A strategy for crowd-creative urban design , 2018 .

[24]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[25]  Mehdi Mekni,et al.  Augmented Reality : Applications , Challenges and Future Trends , 2014 .

[26]  Paul M. Torrens,et al.  Visualizing the City: Communicating Urban Design to Planners and Decision-Makers , 2001 .

[27]  Ariel Noyman,et al.  Finding Places: HCI Platform for Public Participation in Refugees' Accommodation Process , 2018, KES.

[28]  Simon Osindero,et al.  Conditional Generative Adversarial Nets , 2014, ArXiv.

[29]  A Hudson-Smith,et al.  Visual communication in urban planning and urban design , 1998 .

[30]  拓海 杉山,et al.  “Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告 , 2017 .

[31]  Maria Roussou,et al.  Design and Evaluation of a Real-World Virtual Environment for Architecture and Urban Planning , 2007, PRESENCE: Teleoperators and Virtual Environments.

[32]  Sebastian Nowozin,et al.  Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks , 2017, ICML.