快速的环境变化增加了对可靠预测长期地理空间数据的迫切需求。然而，由于非线性特征、复杂的空间依赖关系以及外部驱动因素的存在，准确建模时空地理空间动态仍具挑战性。本文提出了一种综合基准测试框架，用于在相同的预处理、训练和测试流程下，比较基于邻域、基于图和基于注意力的时空深度学习模型。

Rapid environmental change has increased the need for predicting the long-term geospatial reliably. However, accurately modeling spatio-temporal geospatial dynamics remains challenging Because of the nonlinearities, complex spatial dependency, and external driving factors, it is difficult to predict. In this paper, a comprehensive benchmarking framework is proposed for the comparison of neighborhood-based, graph-based and attention-based spatiotemporal deep learning models, with the same preprocessing, training and testing procedure..