Automated building classification framework using convolutional neural network. Issue 1 (31st December 2022)

• Record Type: Journal Article
• Authors: Adha, Augusta; Pamuncak, Arya; Qiao, Wen; Laory, Irwanda
• Editors: Brando, Giuseppe
• Is Part Of: Cogent engineering. Volume 9:Issue 1(2022)
• Publication Date: 2022-12-31
• Subjects: Building classification -- convolutional neural network -- transfer learning -- FEMA-154; Engineering -- Periodicals; Technology -- Periodicals; Engineering; Technology; Periodicals; 620
• DOI: 10.1080/23311916.2022.2065900
• Languages: English
• ISSNs: 2331-1916

Abstract

Abstract: Despite extensive study, performing Rapid visual screening is still a challenging task for many countries. The challenges include the lack of trained engineers, limited resources, and a large building inventory to detect. One of the most important aspect in rapid visual screening is to establish the building classification based on the guidelines' specific criteria. This study proposes a general framework based on Convolutional Neural Network to perform automated building classification for the rapid visual screening procedure. The method classifies buildings based on the Federal Emergency Management Agency (FEMA)-154 guidelines and uses transfer learning techniques from a pre-trained network. The Indonesian building portfolio is used as a case study and a dataset of building images generated through web-scraping on Google Search™ engines and Google StreetView™ website is used for the method validation. Results show that the proposed framework has promising potential to automate the building classification based on FEMA-154 guidelines.