Several deep learning methods for spatial data have been developed that report good performance in a big data setting. These methods typically require the choice of an appropriate kernel and some tuning of hyperparameters, which are contributing reasons for poor performance on smaller data sets.

中文翻译：

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用于相关数据空间插值的图卷积网络

已经开发了几种空间数据深度学习方法，在大数据环境中表现良好。这些方法通常需要选择合适的内核并对超参数进行一些调整，这是导致较小数据集上性能不佳的原因。