Spatial interpolation using conditional generative adversarial neural networks,International Journal of Geographical Information Science

当前位置： X-MOL 学术 › Int. J. Geograph. Inform. Sci. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

Spatial interpolation using conditional generative adversarial neural networks
International Journal of Geographical Information Science ( IF 5.7 ) Pub Date : 2019-04-16 , DOI: 10.1080/13658816.2019.1599122
Di Zhu _{1,

2,

3} , Ximeng Cheng ₁ , Fan Zhang _{1,

4} , Xin Yao ₁ , Yong Gao ₁ , Yu Liu ₁

Affiliation

ABSTRACT Spatial interpolation is a traditional geostatistical operation that aims at predicting the attribute values of unobserved locations given a sample of data defined on point supports. However, the continuity and heterogeneity underlying spatial data are too complex to be approximated by classic statistical models. Deep learning models, especially the idea of conditional generative adversarial networks (CGANs), provide us with a perspective for formalizing spatial interpolation as a conditional generative task. In this article, we design a novel deep learning architecture named conditional encoder-decoder generative adversarial neural networks (CEDGANs) for spatial interpolation, therein combining the encoder-decoder structure with adversarial learning to capture deep representations of sampled spatial data and their interactions with local structural patterns. A case study on elevations in China demonstrates the ability of our model to achieve outstanding interpolation results compared to benchmark methods. Further experiments uncover the learned spatial knowledge in the model’s hidden layers and test the potential to generalize our adversarial interpolation idea across domains. This work is an endeavor to investigate deep spatial knowledge using artificial intelligence. The proposed model can benefit practical scenarios and enlighten future research in various geographical applications related to spatial prediction.

更新日期：2019-04-16

点击分享查看原文

点击收藏

阅读更多本刊最新论文本刊介绍/投稿指南

全部期刊列表>>