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Estimating terrestrial water storage (TWS) with high spatial resolution is crucial for hydrological and water resource management. Comparing to traditional in-situ data measurement, observation from space borne sensor such as Gravity Recovery and Climate Experiment (GRACE) satellites is quite effective to obtain a large-scale TWS data. However, the coarse resolution of the GRACE data restricts its application at a local scale. This paper presents three novel convolutional neural network (CNN) based approaches including the Super-Resolution CNN (SRCNN), the Very Deep Super-Resolution (VDSR), and the Residual Channel Attention Networks (RCAN) to spatial downscaling of the monthly GRACE TWS products using the outputs of the Ecological Assimilation of Land and Climate Observations (EALCO) model over Canada. We also compare the performance of CNN-based methods with the empirical linear regression-based downscaling method. All comparison results were evaluated by root mean square error (RMSE) between the reconstructed GRACE TWS and the original one. RMSEs over the matched pixels are 22.3, 14.4, 18.4 and 71.6 mm of SRCNN, VDSR, RCAN and linear regression-based method respectively. Obviously, VDSR shows the best accuracy among all methods. The result shows all CNN-based super resolution methods preform much better than traditional method in spatial downscaling.

This paper investigates the deep neural networks for rapid and accurate detection of building rooftops in aerial orthoimages. The networks were trained using the manually labeled rooftop vector data digitized on aerial orthoimagery covering the Kitchener-Waterloo area. The performance of the three deep learning methods, U-Net, Fully Convolutional Network (FCN), and Deeplabv3+ were compared by training, validation, and testing sets in the dataset. Our results demonstrated that DeepLabv3+ achieved 63.8% in Intersection over Union (IoU), 77.8% in mean IoU (mIoU), 74% in precision, and 78% in F1-score. After improving the performance with focal loss, training loss was greatly cut down and the convergence rate experienced a significant growth. Meanwhile, rooftop detection also achieved higher performance, as Deeplabv3+ reached 93.6% in average pixel accuracy, with 65.4% in IoU, 79.0% in mIoU, 77.6% in precision, and 79.1% in F1-score. Lastly, in order to evaluate the effects of data volume, by changing data volume from 100% to 75% and 50% in ablation study, it shows that when data volume decreased, the performance of extraction also got worse, with IoU, mIoU, precision, and F1-score also mostly decreased.

In order to put forward policy suggestions for improving the service efficiency of public TCM hospitals in Hubei Province, the statistics of 94 public TCM hospitals of Hubei Province in 2017 was collected by using the comprehensive statistical management information system of TCM there, and the service efficiency of TCM hospitals was analyzed with the DEA-BCC model. The research showed, in 2017, the average technical efficiency, pure technical efficiency and scale efficiency of public TCM hospitals in Hubei Province were 0.919, 0.939 and 0.979 respectively. There were 27.7% of hospitals with effective DEA, 39.4% with effective pure technical efficiency, 31.9% with effective scale efficiency, and 61.7% of public TCM hospitals with diminishing returns to scale. The pure technical efficiency is an important factor restricting the effective DEA of TCM hospitals in Hubei Province, and the scale efficiency also has to be improved. The service efficiency of grade Ⅱ hospitals of TCM are better than that of grade III, and hospitals in central regions are better than that of eastern and western regions. Therefore, local hospitals should take tailored reforms to improve service efficiency according to local conditions.

The design and collaboration analysis of maintenance support system network is a critical challenge. So the network model of ship maintenance support system was proposed, it involved all levels of distributed maintenance support participating nodes and formed a net chain structure. The network characteristic was analysed using complex network theory. The impact of topology characteristic and dynamic evolution on the cooperation effectiveness of maintenance support system was discussed. And an optimal evolution algorithm of support network was put up to so that the optimal support network would be designed．