Predicting the futures from previous spatiotemporal data remains a challenging topic. There have been many previous works on predictive learning. However, mainstream models suffer from huge memory usage or the gradient vanishing problem. Enlightened by the idea from the resnet, we propose CostNet, a novel recursive neural network (RNN)-based network, which has a horizontal and vertical cross-connection. The core of this network is a concise unit, named Horizon LSTM with a fast gradient transmission channel, which can extract spatial and temporal representations effectively to alleviate the gradient propagation difficulty. In the vertical direction outside of the unit, we add overpass connections from unit output to the bottom layer, which can capture the short-term dynamics to generate precise predictions. Our model achieves better prediction results on moving-mnist and radar datasets than the state-of-the-art models.

With the diversification of terminal equipment and operating systems, higher requirements are placed on the rendering performance of maps. The traditional map rendering engine relies on the corresponding operating system graphics library, and there are problems such as the inability to cross the operating system, low rendering performance, and inconsistent rendering style. With the development of hardware, graphics processing unit (GPU) appears in various platforms. How to use GPU hardware to improve map rendering performance has become a critical challenge. In order to address the above problems, this study proposes a cross-platform and high-performance map rendering (Graphics Library engine, GL engine), which uses mask drawing technology and texture dictionary text rendering technology. It can be used on different hardware platforms and different operating systems based on the OpenGL graphics library. The high-performance map rendering engine maintains a consistent map rendering style on different platforms. The results of the benchmark experiments show that the performance of GL engine is 1.75 times and 1.54 times better than the general map rendering engine in the iOS system and in the Android system, respectively, and the rendering performance for vector tiles is 11.89 times and 9.52 times better than rendering in the Mapbox in the iOS system and in the Android system, respectively.