TY - GEN
T1 - A Dataset for Training Machine Learning
T2 - 41st Conference on Education and Research in Computer Aided Architectural Design in Europe, eCAADe 2023
AU - Austern, Guy
AU - Yosifof, Roei
AU - Fisher-Gewirtzman, Dafna
N1 - Publisher Copyright:
© 2023, Education and research in Computer Aided Architectural Design in Europe. All rights reserved.
PY - 2023
Y1 - 2023
N2 - Previous studies have described the effects of urban attributes such as the Spatial Openness Index (SOI) on pedestrians’ experience. SOI uses 3-dimensional ray casting to quantify the volume of visible space from a single viewpoint. The higher the SOI value, the higher the perceived openness and the lower the perceived density. However, the ray casting simulation on an urban-sized sampling grid is computationally intensive, making this method difficult to use in real-time design tools. Convolutional Neural Networks (CNN), have excellent performance in computer vision in image processing applications. They can be trained to predict the SOI analysis for large urban fabrics in real-time. However, these supervised learning models need a substantial amount of labeled data to train on. For this purpose, we developed a method to generate a large series of height maps and SOI maps of urban fabrics in New York City and encoded them as images using colour information. These height map - SOI analysis image pairs can be used as training data for a CNN to provide rapid, precise visibility simulations on an urban scale.
AB - Previous studies have described the effects of urban attributes such as the Spatial Openness Index (SOI) on pedestrians’ experience. SOI uses 3-dimensional ray casting to quantify the volume of visible space from a single viewpoint. The higher the SOI value, the higher the perceived openness and the lower the perceived density. However, the ray casting simulation on an urban-sized sampling grid is computationally intensive, making this method difficult to use in real-time design tools. Convolutional Neural Networks (CNN), have excellent performance in computer vision in image processing applications. They can be trained to predict the SOI analysis for large urban fabrics in real-time. However, these supervised learning models need a substantial amount of labeled data to train on. For this purpose, we developed a method to generate a large series of height maps and SOI maps of urban fabrics in New York City and encoded them as images using colour information. These height map - SOI analysis image pairs can be used as training data for a CNN to provide rapid, precise visibility simulations on an urban scale.
KW - CNN
KW - Machine Learning
KW - Perceived Density
KW - Visibility Analysis
UR - http://www.scopus.com/inward/record.url?scp=85172469100&partnerID=8YFLogxK
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AN - SCOPUS:85172469100
SN - 9789491207358
T3 - Proceedings of the International Conference on Education and Research in Computer Aided Architectural Design in Europe
SP - 781
EP - 790
BT - eCAADe 2023 - Digital Design Reconsidered
A2 - Dokonal, Wolfgang
A2 - Hirschberg, Urs
A2 - Wurzer, Gabriel
A2 - Wurzer, Gabriel
Y2 - 20 September 2023 through 22 September 2023
ER -