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AdaLN: A Vision Transformer for Multidomain Learning and Predisaster Building Information Extraction from Images
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Yunhui Guo and Chaofeng Wang and Stella X. Yu and Frank McKenna and Kincho H. Law
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Journal of Computing in Civil Engineering, 36(5):04022024, 2022
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Paper
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Abstract
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Satellite and street view images are widely used in various disciplines as a source of information for understanding the built environment. In natural hazard engineering, high-quality building inventory data sets are crucial for the simulation of hazard impacts and for supporting decision-making. Screening the building stocks to gather the information for simulation and to detect potential structural defects that are vulnerable to natural hazards is a time-consuming and labor-intensive task. This paper presents an automated method for extracting building information through the use of satellite and street view images. The method is built upon a novel transformer-based deep neural network we developed. Specifically, a multidomain learning approach is employed to develop a single compact model for multiple image-based deep learning information extraction tasks using multiple data sources (e.g., satellite and street view images). Our multidomain Vision Transformer is designed as a unified architecture that can be effectively deployed for multiple classification tasks. The effectiveness of the proposed approach is demonstrated in a case study in which we use pretrained models to collect regional-scale building information that is related to natural hazard risks.
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Keywords
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natural hazard engineering, building information extraction, image segmentation, multi-domain vision transformer
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