Note: Image-based Prediction of House Attributes with Deep Learning
Pages 693 - 695
Abstract
We present an image dataset and a deep learning model that enable the prediction of attributes such as floor area for low-rise buildings (i.e., houses). The dataset consists of 34,600 images of 16,403 buildings in the city of Toronto, Canada, each of which is associated with floor area. The ability to predict such an attribute can facilitate accurate, automated city-scale analysis of the built environment, which can then serve as a basis for policy evaluation and recommendation. A deep convolutional neural network is devised for the task, achieving normalized root mean square error (NRMSE) of 34.24% for interior floor area.
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References
[1]
Aldrick Arceo, Melanie Tham, Gursans Guven, Heather L. MacLean, and Shoshanna Saxe. 2021. Capturing variability in material intensity of single-family dwellings: A case study of Toronto, Canada. Resources, Conservation and Recycling 175 (12 2021), 105885. https://doi.org/10.1016/J.RESCONREC.2021.105885
[2]
Wenzhi Cao, Vahid Mirjalili, and Sebastian Raschka. 2020. Rank consistent ordinal regression for neural networks with application to age estimation. Pattern Recognition Letters 140 (2020), 325–331.
[3]
ODDS Cf. 2015. Transforming our world: the 2030 Agenda for Sustainable Development. United Nations: New York, NY, USA(2015).
[4]
Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele. 2016. The Cityscapes Dataset for Semantic Urban Scene Understanding. In Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[5]
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition. 770–778.
[6]
Edgar Hertwich, Reid Lifset, Stefan Pauliuk, Niko Heeren, Saleem Ali, Qingshi Tu, Fulvio Ardente, Peter Berrill, Tomer Fishman, Koichi Kanaoka, 2020. Resource efficiency and climate change: Material efficiency strategies for a low-carbon future.
[7]
Yuanduo Hong, Huihui Pan, Weichao Sun, and Yisong Jia. 2021. Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes. arXiv preprint arXiv:2101.06085(2021).
[8]
Maud Lanau, Gang Liu, Ulrich Kral, Dominik Wiedenhofer, Elisabeth Keijzer, Chang Yu, and Christina Ehlert. 2019. Taking stock of built environment stock studies: Progress and prospects. Environmental Science & Technology 53, 15 (2019), 8499–8515.
[9]
JF Perrault. 2021. Estimating the Structural Housing Shortage in Canada: Are We 100 Thousand or Nearly 2 Million Units Short. Scotiabank, May 12(2021), 2021.
[10]
Natthanij Soonsawad, Raymundo Marcos Martinez, and Heinz Schandl. 2022. Material demand, and environmental and climate implications of Australia’s building stock: Current status and outlook to 2060. Resources, Conservation and Recycling 180 (2022), 106143.
[11]
Mark Swilling, Maarten Hajer, Tim Baynes, Joe Bergesen, Françoise Labbé, J Kaviti Musango, Anu Ramaswami, Blake Robinson, Serge Salat, Sangwon Suh, 2018. The weight of cities: Resource requirements of future urbanization. IRP Reports (2018).
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Published In
June 2022
710 pages
ISBN:9781450393478
DOI:10.1145/3530190
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Published: 29 June 2022
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COMPASS '22: ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies
June 29 - July 1, 2022
WA, Seattle, USA
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