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      Building Information Modeling and Classification by Visual Learning At A City Scale

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          Abstract

          In this paper, we provide two case studies to demonstrate how artificial intelligence can empower civil engineering. In the first case, a machine learning-assisted framework, BRAILS, is proposed for city-scale building information modeling. Building information modeling (BIM) is an efficient way of describing buildings, which is essential to architecture, engineering, and construction. Our proposed framework employs deep learning technique to extract visual information of buildings from satellite/street view images. Further, a novel machine learning (ML)-based statistical tool, SURF, is proposed to discover the spatial patterns in building metadata. The second case focuses on the task of soft-story building classification. Soft-story buildings are a type of buildings prone to collapse during a moderate or severe earthquake. Hence, identifying and retrofitting such buildings is vital in the current earthquake preparedness efforts. For this task, we propose an automated deep learning-based procedure for identifying soft-story buildings from street view images at a regional scale. We also create a large-scale building image database and a semi-automated image labeling approach that effectively annotates new database entries. Through extensive computational experiments, we demonstrate the effectiveness of the proposed method.

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          Learning Deep Features for Discriminative Localization

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            Building instance classification using street view images

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              Streetscore -- Predicting the Perceived Safety of One Million Streetscapes

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                Author and article information

                Journal
                14 October 2019
                Article
                1910.06391
                2bd5a178-2416-4168-bf2b-65578245a476

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                History
                Custom metadata
                33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada
                cs.CV cs.LG

                Computer vision & Pattern recognition,Artificial intelligence
                Computer vision & Pattern recognition, Artificial intelligence

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