Kyle Steinfeld

Death Valley

NeurIPS 2017 Machine Learning for Creativity and Design

Kyle Steinfeld

In work exhibited at the NeurIPS 2017 Machine Learning for Creativity and Design, we developed a process for relating depthmaps extracted from Google Street View panoramas with the corresponding photographic information. A number of separate models were produced using limited geographic areas of selected cites. With these depthmap-to-panoramic cityscape models trained, we are able to generate new images from unrelated depthmaps which resembled photographic images of the selected cities. This is demonstrated using a javascript app (not currently online) and documented in still images and a series of videos.

Dreams May Come

Kyle Steinfeld

This paper argues that prevailing approaches to CAD software have been fashioned to support modes of reasoning only of secondary importance to design activity, and that, due to some recent developments in computer vision, this state of affairs may be about to change. Surveying the current state of CAD tools, a critical position is developed based upon the best current understanding of the cognitive processes related to design.

Ivy - ACADIA 2017

Progress in Developing Practical Applications for a Weighted-Mesh Representation for Use in Generative Architectural Design

Andrei Nejur & Kyle Steinfeld

This paper presents progress in the development of practical applications for graph representations of meshes for a variety of problems relevant to generative architectural design (GAD).

Studies in Small Scale Data

Three Case Studies on Describing Individuals’ Spatial Behaviour in Cities

Lynnette Widder, Jessie Braden, Joy Ko, and Kyle Steinfeld

Big Data has been effectively mined to understand behavioural patterns in cities and to map large-scale trends predicated upon the repeated actions of many aggregated individuals. While acknowledging the vital role that this work has played in harnessing the Urban Internet of Things as a means to ensure efficient and sustainable urban systems, our work seeks to recover a scale of behavioural research associated with earlier, empirical studies on urban networks.