Spatial interpolation using conditional generative adversarial neural networks

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 stati...

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Vydané v:International journal of geographical information science : IJGIS Ročník 34; číslo 4; s. 735 - 758
Hlavní autori: Zhu, Di, Cheng, Ximeng, Zhang, Fan, Yao, Xin, Gao, Yong, Liu, Yu
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Abingdon Taylor & Francis 02.04.2020
Taylor & Francis LLC
Predmet:
Artificial intelligence
Coders
Deep learning
encoder-decoder
Encoders-Decoders
generative adversarial networks
Geostatistics
Heterogeneity
Interpolation
Machine learning
Mathematical models
Neural networks
Spatial data
Spatial interpolation
spatial prediction
Statistical analysis
Statistical models
ISSN:1365-8816, 1362-3087, 1365-8824
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
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ISSN:1365-8816
1362-3087
1365-8824
DOI:10.1080/13658816.2019.1599122