This study introduces a software-based traffic congestion monitoring system. The transportation system controls the traffic between cities all over the world. Traffic congestion happens not only in cities, but also on highways and other places. The current transportation system is not satisfactory in the area without monitoring. In order to improve the limitations of the current traffic system in obtaining road data and expand its visual range, the system uses remote sensing data as the data source for judging congestion. Since some remote sensing data needs to be kept confidential, this is a problem to be solved to effectively protect the safety of remote sensing data during the deep learning training process. Compared with the general deep learning training method, this study provides a federated learning method to identify vehicle targets in remote sensing images to solve the problem of data privacy in the training process of remote sensing data. The experiment takes the remote sensing image data sets of Los Angeles Road and Washington Road as samples for training, and the training results can achieve an accuracy of about 85%, and the estimated processing time of each image can be as low as 0.047 s. In the final experimental results, the system can automatically identify the vehicle targets in the remote sensing images to achieve the purpose of detecting congestion.

High efficiency video coding (HEVC) has been finalized as the most widely utilized video coding standard, jointly developed by ITU-T, VCEG, and MPEG. In HEVC, the quad-tree structure of the coding unit partition is one of the most substantial modules and provides significant coding gains following huge coding time. In this paper, a rate–distortion-based coding unit partition network (RDNet) is proposed to make partition decisions based on the statistical features. RDNet is composed of a prediction sub-network and a target sub-network, where the prediction sub-network is used to predict the CU partition modes of the intra-prediction and the target sub-network is designed to optimize the network parameters by evaluating the rate–distortion cost, respectively. To balance the prediction accuracy and the rate–distortion loss, a parameter-exchanging strategy is applied to control the parameters’ sharing between two networks. Experimental results prove that our model can reduce the encoding time of HEVC by 55.83~71.72% with an efficient BD-BR of 2.876~3.347%, and the ablation study evaluates the ability of our strategy on balancing the trade-off between coding accuracy and inference speed.