Complex Event Analysis of Urban Environmental Data based on Deep CNN of Spatiotemporal Raster Images

Abstract

Torrential rains, the complicated network of roads, and the high density of vehicles contribute partly the number of traffic accidents. In order to understand the association between these factors towards building a risk map that can alert drivers of dangerous zones, the visual patterns reasoning system is proposed. By converting sensing data collected from different factors to raster images, the associations can be treated as visual patterns that can conserve their spatiotemporal information. Deep convolutional neural networks (Deep-CNN) are utilized to build a model based on these raster images towards detecting accidents based on the association between factors. Image clustering is applied to learn a representation of each type of associations. Thus, the visual pattern of high-probability traffic accidents can be reasoned in the natural language format. Both 2D and 3D raster images are investigated to examine the spatial and spatiotemporal associations between factors with Deep-CNN models. We use both transfer learning and fine-tuning approach to build our model due to the small size of the positive samples dataset. The evaluation shows the initial but promising results of our method. We also discuss the potential applications and various research directions can be investigated using our proposed method.