干旱区生态环境知识资源中心(Arid): Spatial Variability Aware Deep Neural Networks (SVANN): A General Approach

Arid

DOI	10.1145/3466688
	Spatial Variability Aware Deep Neural Networks (SVANN): A General Approach
	Gupta, Jayant; Molnar, Carl; Xie, Yiqun; Knight, Joe; Shekhar, Shashi
通讯作者	Gupta, J (corresponding author)，Univ Minnesota, Dept Comp Sci & Engn, 4-192 Keller Hall,200 Union St SE, Minneapolis, MN 55455 USA.
来源期刊	ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
ISSN	2157-6904
EISSN	2157-6912
出版年	2021
卷号	12 期号:6
英文摘要	Spatial variability is a prominent feature of various geographic phenomena such as climatic zones, USDA plant hardiness zones, and terrestrial habitat types (e.g., forest, grasslands, wetlands, and deserts). However, current deep learning methods follow a spatial-one-size-fits-all (OSFA) approach to train single deep neural network models that do not account for spatial variability. Quantification of spatial variability can be challenging due to the influence of many geophysical factors. In preliminary work, we proposed a spatial variability aware neural network (SVANN-I, formerly called SVANN) approach where weights are a function of location but the neural network architecture is location independent. In this work, we explore a more flexible SVANNE approach where neural network architecture varies across geographic locations. In addition, we provide a taxonomy of SVANN types and a physics inspired interpretation model. Experiments with aerial imagery based wetland mapping show that SVANN-I outperforms OSFA and SVANN-E performs the best of all.
英文关键词	Neural networks spatial variability
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000754524700009
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Information Systems
WOS研究方向	Computer Science
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/375861
作者单位	[Gupta, Jayant; Molnar, Carl; Shekhar, Shashi] Univ Minnesota, Dept Comp Sci & Engn, 4-192 Keller Hall,200 Union St SE, Minneapolis, MN 55455 USA; [Xie, Yiqun] Univ Maryland, Ctr Geospatial Informat Sci, Dept Geog Sci, 1124 Lefrak Hall,7251 Preinkert Dr, College Pk, MD 20742 USA; [Knight, Joe] Univ Minnesota, Dept Forest Resources, 1530 Cleveland Ave N, St Paul, MN 55108 USA
推荐引用方式 GB/T 7714	Gupta, Jayant,Molnar, Carl,Xie, Yiqun,et al. Spatial Variability Aware Deep Neural Networks (SVANN): A General Approach[J],2021,12(6).
APA	Gupta, Jayant,Molnar, Carl,Xie, Yiqun,Knight, Joe,&Shekhar, Shashi.(2021).Spatial Variability Aware Deep Neural Networks (SVANN): A General Approach.ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY,12(6).
MLA	Gupta, Jayant,et al."Spatial Variability Aware Deep Neural Networks (SVANN): A General Approach".ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY 12.6(2021).