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Xiayin Lou

As seen in: MDPI, Taylor & Francis Online
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Articles

GeoXCP: uncertainty quantification of spatial explanations in explainable AI

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By Xiayin Lou, Peng Luo, Ziqi Li, Song Gao
| Taylor & Francis Online
?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom. Abstract Understanding and explaining complex geographic phenomena—ranging from climate change to socioeconomic disparities—is a central focus in both geography and the broader scientific community.
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GeoConformal Prediction: A Model-Agnostic Framework for Measuring the Uncertainty of Spatial Prediction

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By Xiayin Lou, Peng Luo, Liqiu Meng
| Taylor & Francis Online
?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom. Abstract Spatial prediction is a fundamental task in geography, providing essential data support for various scenarios.
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Nighttime Thermal Infrared Image Translation Integrating Visible Images

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By Shihao Yang, Min Sun, Xiayin Lou, Hanjun Yang
| MDPI
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
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