SAFNet: Artificial Intelligence–Based Multisource Heterogeneous Data Fusion for Comprehensive Building Attribute Prediction

dc.contributor.author	Subedi, Abhishek
dc.contributor.author	Jahanshahi, Mohammad R.
dc.contributor.author	Johnson, David R.
dc.date.accessioned	2026-08-20T10:40:28Z
dc.date.available	2026-08-20T10:40:28Z
dc.date.copyright	2025/11/20
dc.date.issued	2025
dc.identifier.other	AOMJAH.AOENG-0089.pdf
dc.identifier.uri	http://yetl.yabesh.ir/yetl1/handle/yetl/4311104
dc.description.abstract	Abstract Accurate assessment of structural attributes is vital for flood risk evaluation and mitigation planning. Manual surveys for such data are expensive and time-consuming, while automated methods often rely solely on image-based models or narrowly ...
dc.publisher	American Society of Civil Engineers
dc.title	SAFNet: Artificial Intelligence–Based Multisource Heterogeneous Data Fusion for Comprehensive Building Attribute Prediction
dc.type	Journal Article
dc.journal.volume	3
dc.journal.issue	1
dc.journal.title	ASCE OPEN: Multidisciplinary Journal of Civil Engineering
dc.identifier.doi	10.1061/AOMJAH.AOENG-0089
dc.journal.fristpage	04025012-1
dc.journal.lastpage	04025012-23
dc.page	23
dc.tree	ASCE OPEN: Multidisciplinary Journal of Civil Engineering:;2025:;Volume ( 003 ):;issue: 001
dc.contenttype	Fulltext