AdaLN: A Vision Transformer for Multidomain Learning and Predisaster Building Information Extraction from Images
AdaLN: A Vision Transformer for Multidomain Learning and Predisaster Building Information Extraction from Images
复制标题
DOI：
10.1061/(asce)cp.1943-5487.0001034
复制
发表时间：
2022-09-01
影响因子：
6.9
通讯作者：
Law, Kincho H. H.
中科院分区：
文献类型：
--
作者：
Guo, Yunhui;Wang, Chaofeng;Law, Kincho H. H.
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.
