Real-time prediction of spatial raster time series, such as those derived from satellite remote sensing imagery, is important for making emergency decisions on various geo-spatial processes/events. However, because of the scalability issue and large training time requirement, the neural network (NN)-based models often fail to perform real-time prediction, in spite of their tremendous potential. In this paper, we propose ContRast, a variant of recurrent NN-based context-aware raster time series prediction model that attempts to resolve these issues by: (1) eliminating the need for offline adjustment of network structure by employing self-evolving autonomous learning of recurrent neural network, (2) saving training time by adopting single-pass parameter learning mechanism, and (3) reducing redundant learning by skipping sub-regional data associated with similar spatio-temporal context and reusing already learned parameters to predict for the same. Experimental evaluations with respect to predicting normalized difference vegetation index (NDVI)-raster derived from MODIS Terra satellite remote sensing imagery show that ContRast is highly effective for real-time prediction of spatial raster time series, and it significantly outperforms the existing models. In addition, the theoretical analyses of model complexity and computational cost further justify our empirical observations.

实时预测空间栅格时间序列，例如从卫星遥感影像中得出的时间，对于做出各种地理空间过程/事件的应急决策非常重要。但是，由于可伸缩性问题和大量的训练时间要求，基于神经网络（NN）的模型尽管具有巨大的潜力，但通常仍无法执行实时预测。在本文中，我们提出了ContRast，这是基于NN的递归上下文栅格时间序列预测模型的一种变体，该模型试图通过以下方法解决这些问题：（1）通过采用自我进化的自主学习消除了对网络结构的离线调整的需求递归神经网络；（2）通过采用单遍参数学习机制节省训练时间；（3）通过跳过与类似时空上下文相关联的子区域数据并重用已经学习的参数进行预测来减少冗余学习。从MODIS Terra卫星遥感影像获得的关于预测归一化植被指数（NDVI）-栅格的实验评估表明，ContRast对空间栅格时间序列的实时预测非常有效，并且其性能明显优于现有模型。此外，对模型复杂性和计算成本的理论分析进一步证明了我们的经验观察。从MODIS Terra卫星遥感影像获得的关于预测归一化植被指数（NDVI）-栅格的实验评估表明，ContRast对空间栅格时间序列的实时预测非常有效，并且其性能明显优于现有模型。此外，对模型复杂性和计算成本的理论分析进一步证明了我们的经验观察。从MODIS Terra卫星遥感影像获得的关于预测归一化植被指数（NDVI）-栅格的实验评估表明，ContRast对空间栅格时间序列的实时预测非常有效，并且其性能明显优于现有模型。此外，对模型复杂性和计算成本的理论分析进一步证明了我们的经验观察。