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This paper proposes a deep convolutional neural network model with encoder-decoder architecture to extract road network from satellite images. We employ ResNet-18 and Atrous Spatial Pyramid Pooling technique to trade off between the extraction precision and running time. A modified cross entropy loss function is proposed to train our deep model. A PointRend algorithm is used to recover a smooth, clear and sharp road boundary. The augmentated DeepGlobe dataset is used to train our deep model and the asynchronous training method is applied to accelerate the training process. Five salellite images covering Xiaomu village are taken as input to evaluate our model. The proposed E-Road model has fewer number of parameters and shorter training time. The experiments show E-Road outperforms other state-of-the-art deep models with 5.84% to 59.09% improvement, and can give the accurate predictions for the images with complex environment. [ABSTRACT FROM AUTHOR]

This paper presents a dynamic health intelligent evaluation model proposed to analyze the health deterioration of satellites under time-varying and extreme thermal loads. New definitions such as health degree and failure factor and new topological system considering the reliability relationship are proposed to characterize the dynamic performance of health deterioration. The dynamic health intelligent evaluation model used the thermal network method (TNM) and fuzzy reasoning to solve the problem of model missing and non-quantization between temperature and failure probability, and it can quickly evaluate and analyze the dynamic health of satellite through the collaborative processing of continuous event and discrete event. In addition, the temperature controller in the thermal control subsystem (TCM) is the target of thermal damage, and the effects of different heat load amplitude, duty ratio, and cycle on its health deterioration are compared and analyzed. [ABSTRACT FROM AUTHOR]