Among the various challenges that spaceborne radar observations of the ocean face, the following two issues are probably of a higher priority: inadequate dynamic resolution, and ineffective vertical penetration. It is therefore the vision of the National Laboratory for Marine Science and Technology of China that two highly anticipated breakthroughs in the coming decade are likely to be associated with radar interferometry and ocean lidar (OL) technology, which are expected to make a substantial contribution to a submesoscale-resolving and depth-resolving observation of the ocean. As an expanded follow-up of SWOT and an oceanic counterpart of CALIPSO, the planned "Guanlan" science mission comprises a dual-frequency (Ku and Ka) interferometric altimetry (IA), and a near-nadir pointing OL. Such an unprecedented combination of sensor systems has at least three prominent advantages. (i) The dual-frequency IA ensures a wider swath and a shorter repeat cycle which leads to a significantly improved temporal and spatial resolution up to days and kilometers. (ii) The first spaceborne active OL ensures a deeper penetration depth and an all-time detection which leads to a layered characterization of the optical properties of the subsurface ocean, while also serving as a near-nadir altimeter measuring vertical velocities associated with the divergence, and convergence of geostrophic eddy motions in the mixed layer. (iii) The simultaneous functioning of the IA/OL system allows for an enhanced correction of the contamination effects of the atmosphere and the air-sea interface, which in turn considerably reduces the error budgets of the two sensors. As a result, the integrated IA/OL payload is expected to resolve the ocean variability at submeso and sub-week scales with a centimeter-level accuracy, while also partially revealing marine life systems and ecosystems with a 10-m vertical interval in the euphotic layer, moving a significant step forward toward a "transparent ocean" down to the vicinity of the thermocline, both dynamically and bio-optically.

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Dynamic hand gesture recognition based on one-shot learning requires full assimilation of the motion features from a few annotated data. However, how to effectively extract the spatio-temporal features of the hand gestures remains a challenging issue. This paper proposes a skeleton-based dynamic hand gesture recognition using an enhanced network (GREN) based on one-shot learning by improving the memory-augmented neural network, which can rapidly assimilate the motion features of dynamic hand gestures. Besides, the network effectively combines and stores the shared features between dissimilar classes, which lowers the prediction error caused by the unnecessary hyper-parameters updating, and improves the recognition accuracy with the increase of categories. In this paper, the public dynamic hand gesture database (DHGD) is used for the experimental comparison of the state-of-the-art performance of the GREN network, and although only 30% of the dataset was used for training, the accuracy of skeleton-based dynamic hand gesture recognition reached 82.29% based on one-shot learning. Experiments with the Microsoft Research Asia (MSRA) hand gesture dataset verified the robustness of the GREN network. The experimental results demonstrate that the GREN network is feasible for skeleton-based dynamic hand gesture recognition based on one-shot learning.

Eddies can be identified and tracked based on satellite altimeter data. However, few studies have focused on nowcasting the evolution of eddies using remote sensing data. In this paper, an improved Convolutional Long Short-Term Memory (Conv-LSTM) network named Prednet is used for eddy nowcasting. Prednet, which uses a deep, recurrent convolutional network with both bottom-up and top-down connects, has the ability to learn the temporal and spatial relationships associated with time series data. The network can effectively simulate and reconstruct the spatiotemporal characteristics of the future sea level anomaly (SLA) data. Based on the SLA data products provided by Archiving, Validation, and Interpretation of Satellite Oceanographic (AVISO) from 1993 to 2018, combined with an SLA-based eddy detection algorithm, seven-day eddy nowcasting experiments are conducted on the eddies in South China Sea. The matching ratio is defined as the percentage of true eddies that can be successfully predicted by Conv-LSTM network. On the first day of the nowcasting, matching ratio for eddies with diameters greater than 100 km is 95%, and the average matching ratio of the seven-day nowcasting is approximately 60%. In order to verify the performance of nowcasting method, two experiments were set up. A typical anticyclonic eddy shedding from Kuroshio in January 2017 was used to verify this nowcasting algorithm’s performance on single eddy, with the mean eddy center error is 11.2 km. Moreover, compared with the eddies detected in the Hybrid Coordinate Ocean Model data set (HYCOM), the eddies predicted with Conv-LSTM networks are closer to the eddies detected in the AVISO SLA data set, indicating that deep learning method can effectively nowcast eddies.

The mission of Surface Water and Ocean Topography (SWOT) is scheduled to be launchedin 2022, and global ocean eddies with radius scales of larger than 10 km are expected to be observedfrom space. However, there are still open questions about the capability of SWOT to detect oceaneddies. Based on ocean model data and SWOT orbit, this study simulates along-track observationof SWOT. Two eddy datasets are derived from simulated observation data via mapping and eddyidentification procedures, one of which includes SWOT errors and the other does not. The thirdeddy dataset is generated from the original model data. Through comparing these three eddydatasets, it is found that 34% (40%) eddies are lost due to insufficient temporal sampling and errorsin the Kuroshio Extension (South China Sea) region, and numerous artifact eddies are generated.To further explain the influence of SWOT errors on smaller-scale eddies, two eddies (a cyclonic eddyand an anticyclonic eddy) with the radius of about 10 km are repeatedly observed 100 times usingthe SWOT-simulator. The cyclonic eddy with larger amplitude has been detected 84 times, whilethe anticyclonic eddy is visible 76 times. Therefore, the influence of the SWOT sampling and errorson ocean eddy observation is revealed by the results of these observing system simulationexperiments (OSSEs).

Intelligent recognition of traffic police command gestures increases authenticity and interactivity in virtual urban scenes. To actualize real-time traffic gesture recognition, a novel spatiotemporal convolution neural network (ST-CNN) model is presented. We utilized Kinect 2.0 to construct a traffic police command gesture skeleton (TPCGS) dataset collected from 10 volunteers. Subsequently, convolution operations on the locational change of each skeletal point were performed to extract temporal features, analyze the relative positions of skeletal points, and extract spatial features. After temporal and spatial features based on the three-dimensional positional information of traffic police skeleton points were extracted, the ST-CNN model classified positional information into eight types of Chinese traffic police gestures. The test accuracy of the ST-CNN model was 96.67%. In addition, a virtual urban traffic scene in which real-time command tests were carried out was set up, and a real-time test accuracy rate of 93.0% was achieved. The proposed ST-CNN model ensured a high level of accuracy and robustness. The ST-CNN model recognized traffic command gestures, and such recognition was found to control vehicles in virtual traffic environments, which enriches the interactive mode of the virtual city scene. Traffic command gesture recognition contributes to smart city construction.

temperature, seasonal variability of salinity in the oceanic mixed layer has a clear tropical dominance. Meanwhile, it is found that a two-mode structure with annual and semiannual periodicities can effectively penetrate through the upper ocean into a depth of ~2000 m. Fourth, signature of Rossby waves is identified in the annual phase map of ocean salinity within 200-600 m depths in the tropical oceans, revealing a strongly co-varying nature of ocean temperature and salinity at specific depths. These results serve as significant contributions to improving our knowledge on the haline aspect of the ocean climate.

This paper introduces a virtual city oriented VR-GIS platform which synthesizes several latest information technologies including virtual reality, 3D geographical information system, remote sensing, and multi-dimensional visualization. The platform is a seamless integration of VR functions and GIS analysis methods, which can be used to organize and present massive spatial data. It also supplies 3D spatial analysis functions, 3D visualization for spatial process and natural simulation, and serves as an engine platform for digital city.