AUTHOR=Ghione Federica , Mæland Steffen , Meslem Abdelghani , Oye Volker TITLE=Building Stock Classification Using Machine Learning: A Case Study for Oslo, Norway JOURNAL=Frontiers in Earth Science VOLUME=10 YEAR=2022 URL=https://www.frontiersin.org/journals/earth-science/articles/10.3389/feart.2022.886145 DOI=10.3389/feart.2022.886145 ISSN=2296-6463 ABSTRACT=
This paper describes a new concept to automatically characterize building types in urban areas based on publicly available image databases, making parts of seismic risk assessment more time and cost-effective, and improving the reliability of seismic risk assessment, especially in regions where building stock information is currently not documented. One of the main steps in evaluating potential human and economic losses in a seismic risk assessment, is the development of inventory databases for existing building stocks in terms of load-resisting structural systems and material characteristics (building typologies classification). The common approach for building stock model classification is to perform extensive fieldwork and walk-down surveys in representative areas of a city, and in some cases using random sample surveys of geounits. This procedure is time and cost consuming, and subject to personal interpretation: to mitigate these costs, we have introduced a machine learning methodology to automate this classification based on publicly available image databases. We here use a Convolutional Neural Network (CNN) to automatically identify the different building typologies in the city of Oslo, Norway, based on facade images taken from