LAUSR

The rapid development of remote sensing technology has brought abundant data support for deep learning based temperature forecasting research. However, recently proposed methods usually focus on the temporal relationship among temperature observation information, whereas ignore the spatial positions of different regions. Motivated by the observation that adjacent regions usually present similar temperature trends, in this article, we consider the temperature forecasting as a spatiotemporal sequence prediction problem, and propose a new deep learning model for temperature forecasting, self-attention joint spatiotemporal network (SA-JSTN), which simultaneously captures the spatiotemporal interdependency information. The kernel component of the SA-JSTN is a newly developed spatiotemporal memory (STM) unit, which describes the temporal and spatial models via a unified memory cell. STM is constructed based on the units of the convolutional long short-term memory (ConvLSTM). Instead of using simple convolutions for spatial information extraction, in STM, we improve ConvLSTM by a self-attention module, which has significantly enhanced the global spatial information representation ability of our proposed network. Compared with other deep learning based temperature forecasting methods, SA-JSTN is able to integrate the global spatial correlation into the temperature series prediction problem, and thus present better performance especially in short-term prediction. We have conducted comparison experiments on two typical temperature datasets to validate the effectiveness of our proposed method.