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Spatial Interpolation With Locally Varying Anisotropy Using Deep Neural Networks

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成果类型:
期刊论文
作者:
Jinbiao Yan*;Xinyou Chen;Jiajun Liu;Bo Wu
通讯作者:
Jinbiao Yan
作者机构:
[Bo Wu] School of Geography and Environment, Jiangxi Normal University, Nanchang, China
[Jinbiao Yan; Xinyou Chen; Jiajun Liu] College of Geography and Tourism, Hengyang Normal University, Hengyang, China
通讯机构:
[Jinbiao Yan] C
College of Geography and Tourism, Hengyang Normal University, Hengyang, China
语种:
英文
关键词:
deep neural network;local varying anisotropy;nonlinearity;spatial interpolation
期刊:
Transactions in GIS
ISSN:
1361-1682
年:
2025
卷:
29
期:
7
页码:
e70140
基金类别:
This work was supported by the National Natural Science Foundation of China (Grant 42371419), Natural Science Foundation of Hunan Province, China (Grant 2023JJ40100), Scientific Research Fund of Hunan Provincial Education Department, China (Grant 22A0498), Scientific Research Fund of Hengyang Normal University, China (Grant 2023QD10).
机构署名:
本校为通讯机构
院系归属:
地理与旅游学院
摘要:
In spatial interpolation, both distance and direction play critical roles. While Euclidean distance captures only isotropic spatial characteristics, extended methods have introduced non-Euclidean metrics to better reflect spatial anisotropy. However, most existing approaches primarily address global anisotropy, typically assuming that anisotropic parameters—such as scaling and principal direction—are spatially uniform. Consequently, these methods are often inadequate in scenarios where anisotropic properties vary across space. To address this...

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