Weimin Huang, Alexander W. Olson, Elias Boutros Khalil, Shoshanna Saxe
{"title":"注:基于图像的深度学习房屋属性预测","authors":"Weimin Huang, Alexander W. Olson, Elias Boutros Khalil, Shoshanna Saxe","doi":"10.1145/3530190.3534828","DOIUrl":null,"url":null,"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.","PeriodicalId":257424,"journal":{"name":"ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Note: Image-based Prediction of House Attributes with Deep Learning\",\"authors\":\"Weimin Huang, Alexander W. Olson, Elias Boutros Khalil, Shoshanna Saxe\",\"doi\":\"10.1145/3530190.3534828\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":257424,\"journal\":{\"name\":\"ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)\",\"volume\":\"37 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-06-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3530190.3534828\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3530190.3534828","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Note: Image-based Prediction of House Attributes with Deep Learning
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.