Advances in spatiotemporal graph neural network prediction research

Being a kind of non-Euclidean data,spatiotemporal graph data exists everywhere from trafficflow,air quality index to crime case,etc.Unlike the raster data,the irregular and disordered characteristics of spatiotemporal graph data have attracted the research interest of scholars,with the prediction of spatiotemporal graph data being one of the research hot spots.The emergence of spatiotemporal graph neural networks(ST-GNNs)provides a new insight for solving the problem of obtaining spatial correlation for spatiotemporal graph data prediction while achieving state-of-the-art performance.In this paper,comprehensive survey of research on ST-GNNs prediction domain isa presented,where the background of ST-GNNs is introduced before the computational paradigm of ST-GNN is thoroughly reviewed.From the perspective of model construction,59 well-known models in recent years are classified and discussed.Some of these models are further analyzed in terms of performance and efficiency.Subsequently,the categories and applicationfields of spatiotemporal graph data are summarized,providing a clear idea of technology selection for different applications.Finally,the evolution history and future direction of ST-GNNs are also summarized,to facilitate future researchers to timely understand the current state of prediction research by ST-GNNs.

A high-precision image classification network model based on a voting mechanism

With the development of satellite remote sensing technology,image classification task,as the basis of remote sensing data interpretation,has received wide attention to improving accuracy and robustness.At the same time,in-depth learning technology has been widely used in remote sensing and has a far-reaching impact.Since the existing image classification methods ignore the feature that the general image semantics are the same as the semantics of a single pixel,this paper presents an algorithm that uses the semantics of an image to achieve high-precision image classification.Based on the idea of partial substitution for global,this algorithm designs a split result voting mechanism and builds a Vgg-Vote network model.This mechanism votes on the semantically segmented result of an image and uses the maximum filtering function to select the category containing the most significant number of pixels as the prediction category of the image.Experiments on UC Merced Land-User complete datasets and five types of incomplete datasets with varying degrees of interference,including noise,data occlusion and loss,show that the Vote mechanism dramatically improves the classification accuracy,robustness and anti-jamming capability of Vgg-Vote.