Associative Trails

Blog

A selection of thoughts, notes on other people's thoughts, experiences with APIs, tips, tricks, and other stuff.

Deep Learning Architect - Classification for Architectural Design through the Eye of Artificial Intelligence (2018)
Notes on a 2018 paper by Yuji Yoshimura, Bill Cai, Zhoutong Wang, and Carlo Ratti

Using a convolutional neural network to recognise top-level architects' individual styles. Almost 70% or architects can be distinguished with 80% accuracy. 20,000 sample images by 34 past Prisker Prize winners.

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Architectural Drawings Recognition and Generation through Machine Learning (2018)
Notes on a 2018 paper by Weixin Huang and Hao Zheng

A generative adversarial network to superimpose coloured rectangles on architectural floor plans based on room type (Bedroom, kitchen, etc.). Worked really well for some rooms - it even picked up a few mistakes in the human labelling.

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OPTIMISING IMAGE CLASSIFICATION - Implementation of Convolutional Neural Network Algorithms to Distinguish Between Plans and Sections within the Architectural, Engineering and Construction (AEC) Industry (2019)
Notes on a 2019 paper by Alessandra Fabbri, M. Hank Haeusler, and Yannis Zavoleas

An attempt to build a convolutional neural network to classify 1000 images into 3 categories. Accuracy peaked at 82%, no hyperparameter tuning was performed.

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Drawing Recognition. Integrating Machine Learning Systems into Architectural Design Workflows (2020)
Notes on a 2020 paper by Lachlan Brown, Michael Yip, Nicole Gardner, M. Hank Haeusler Haeusler, Nariddh Khan, Yannis Zavoleas, and Christina Ramos

Development of a convolutional neural network for the recognition and labelling of rooms within floor plan images of multi-residential apartments. Trains on 454 floor plan images sorting into 6 categories - claims the results were "satisfactory".

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Classification of Building Information Model (BIM) Structures with Deep Learning (2018)
Notes on a 2018 paper by Francesco Lomio, Ricardo Farinha, Mauri Laasonen, and Heikki Huttunen

A comparison of different machine learning methods and their accuracy in categorising building use. The deep learning models came out on top, with the model based on ResNet50 performing particularly well. 240 images were used to train the models to sort into three categories.

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