An Optimization Method for Virtual Globe Ocean Surface Dynamic Visualization

The existing visualization method in the virtual globe mainly uses the projection grid to organize the ocean grid. This special grid organization has the defects in reflecting the difference characteristics of different ocean areas. The method of global ocean visualization based on global discrete grid can make up the defect of the projection grid method by matching with the discrete space of the virtual globe, so it is more suitable for the virtual ocean surface simulation application.But the available global discrete grids method has many problems which limiting its application such as the low efficiency of rendering and loading, the need of repairing grid crevices. To this point, we propose an optimization for the global discrete grids method. At first, a GPU-oriented multi-scale grid model of ocean surface which develops on the foundation of global discrete grids was designed to organize and manage the ocean surface grids. Then, in order to achieve the wind-drive wave dynamic rendering, this paper proposes a dynamic wave rendering method based on the multi-scale ocean surface grid model to support real-time wind field updating. At the same time, considering the effect of repairing grid crevices on the system efficiency, this paper presents an efficient method for repairing ocean surface grid crevices based on the characteristics of ocean grid and GPU technology. At last, the feasibility and validity of the method are verified by the comparison experiment. The experimental results show that the proposed method is efficient, stable and fast, and can compensate for the lack of function of the existing methods, so the application range is more extensive

A multi-scale VR navigation method for VR globes

The combination of virtual reality (VR) and virtual globes – VR globes – enables users to not only view virtual scenes in an immersive manner at any location on Earth but also directly interact with multi-scale spatial data using natural behaviors. It is an important direction for the future development of 3D GIS and geovisualization. However, current VR navigation are primarily based on small real spaces. For virtual globes, which are 3D multi-scale globe environment, the realization of VR navigation in the multi-scale virtual globe space within a limited real space is the first problem that needs to be addressed. A multi-scale VR navigation method that consists of two algorithms is proposed in this study. The first algorithm maps the real space to the virtual globe space and connects the VR user with the VR viewpoint. The second algorithm is an octree structure-based viewpoint correction algorithm that is proposed to correct the location of the moving VR viewpoint in real time. The proposed method is validated by experimentation. The experimental results indicate that the proposed method enables a VR user to interactively view the 3D multi-scale globe environment and lays a foundation for human–computer interaction in VR globes

A virtual globe-based time-critical adaptive visualization method for 3D city models

3D city models, which are important items of content on the virtual globe, are characterized by complicated structures and large amounts of data. These factors make the visualization of 3D city models highly dependent upon the performance of computer hardware. Thus, achieving the efficient rendering of 3D city models using different hardware performance levels represents one of the key problems currently facing researchers. This paper proposes a time-critical adaptive visualization method that first estimates the possible rendering time for each model according to the data structure of the model in addition to the CPU/GPU performance of the computer. It then dynamically adjusts the rendering level for each model based on the results of an estimation of the rendering time to ensure that the final scene can be completed within a given time. To verify the effectiveness and flexibility of this method, it is applied using different computers. The results show that the adaptive visualization method presented in this paper not only can adapt to computers with different levels of performances but also demonstrates an obvious improvement in the time estimation precision, visual effects, and optimization speed relative to existing adaptive visualization methods

Visualizing Large-Scale Building Information Modeling Models within Indoor and Outdoor Environments Using a Semantics-Based Method

Building information modeling (BIM), with detailed geometry and semantics of the indoor environment, has become an essential part of smart city development and city information modeling (CIM). However, visualizing large-scale BIM models within geographic information systems (GIS), such as virtual globes, remains a technological challenge with limited hardware resources. Previous methods generally removed indoor features in a single-source (BIM) scene to reduce the computational burden from outdoor views, which have not been applied to the multi-source and -scale geographic environment (e.g., virtual globes). This approach neglected special BIM semantics (e.g., transparent windows), which may miss a part of geographic features or buildings and cause unreasonable visualization. Besides, the method overlooked indoor visualization optimization, which may burden computing resources when visualizing big and complex buildings from indoor views. To address these problems, we propose a semantics-based method for visualizing large-scale BIM models within indoor and outdoor environments. First, we organize large-scale BIM models based on a latitude-longitude grid (LLG) in the outdoor environment; a multilayer cell-and-portal graph is used to index the structure of the BIM model and building entities. Second, we propose a scheduling algorithm to achieve the integrated visualization in indoor and outdoor environments considering BIM semantics. The application of the proposed method to a multi-scale and -source environment confirmed that it can achieve an effective and efficient visualization for huge BIM models in indoor-outdoor scenes. Compared with the previous study, the proposed method considers the BIM semantics and thus can visualize more complete features from outdoor and indoor views of BIM models in the virtual globe. Besides, the study only loads as visible data as possible, which can retain lower the volume of increased geometry, and thus keep a higher frame rate for the tested areas

A binocular parallel rendering method for VR globes

The scene-rendering mechanism based on binocular vision is one of the key techniques for the VR globe to achieve immersion-type visualization of global 3D scenes. However, this special rendering mechanism also requires that the 3D scene is continuously drawn twice within one frame, which significantly affects the rendering efficiency of VR globes. Therefore, we propose a binocular parallel rendering method. This method first improves the current rendering process of VR globes by assigning the rendering tasks for the left and right camera of VR to be processed on different CPU cores, thereby achieving parallel rendering of binocular scenes. Second, due to the problem of inconsistent resolution of binocular scenes caused by different viewpoints for the left and right cameras, we propose a resolution synchronize algorithm. this algorithm conducts real-time synchronization on the resolution of scene in the rendering process and thus avoids the problem of erroneous binocular stereo matching. Finally, we validate the effectiveness of the method in this paper through experiments. The results of experiments indicate that while the method in this paper can ensure the consistency of binocular scene resolution, it can decrease the frame time of VR globes by approximately 27% on average

A Novel Spectral Indices-Driven Spectral-Spatial-Context Attention Network for Automatic Cloud Detection

Cloud detection is a fundamental step for optical satellite image applications. Existing deep learning methods can provide more accurate cloud detection results. However, the performance of these methods relies on a large number of label samples, whose collection is time-consuming and high-cost. In addition, cloud detection is challenging in high-brightness scenes due to cloud and high-brightness objects having a similar spectral features. In this study, we propose a cloud index driven spectral-spatial-context attention network (SSCA-net) for cloud detection, which relies on no effort to manually collect label samples and can improve the accuracy of cloud detection in high-brightness scenes. The label samples are automatically generated from the cloud index by using dual-threshold, which is then expanded to improve the completeness of cloud mask labels. We designed SSCA-net with the spectral-spatial-context aware module and spectral-spatial-context information aggregation module, aimed to improve the accuracy of cloud detection in high-brightness scenes. The results show that the proposed SSCA-net achieved good performance with an average overall accuracy of 97.69% and an average kappa coefficient of 92.71% on the Sentinel-2 and Landsat-8 datasets. This article provides fresh insight into how advanced deep attention networks and cloud indexes can be integrated to obtain high accuracy of cloud detection on high-brightness scenes

Effect of population migration and socioeconomic factors on the COVID-19 epidemic at county level in Guangdong, China

Coronavirus disease 2019 (COVID-19) has become a major public health concern worldwide. In this study, we aimed to analyze spatial clusters of the COVID-19 epidemic and explore the effects of population emigration and socioeconomic factors on the epidemic at the county level in Guangdong, China. Data on confirmed cases, population migration, and socioeconomic factors for 121 counties were collected from 1 December 2019 to 17 February 2020, during which there were a total of 1,328 confirmed cases. County-level infected migrants of Guangdong moving from Hubei were calculated by integrating the incidence rate, population migration data of Baidu Qianxi, and the resident population. Using the spatial autocorrelation method, we identified high-cluster areas of the epidemic. We also used a geographical detector to explore infected migrants and socioeconomic factors associated with transmission of COVID-19 in Guangdong. Our results showed that: 1) the epidemic exhibited significant positive global spatial autocorrelation; high–high spatial clusters were mainly distributed in the Pearl River Estuary region; 2) city-level population migration data corroborated with the incidence rate of each city in Hubei showed significant association with confirmed cases; 3) in terms of potential factors, infected migrants greatly contributed to the spread of COVID-19, which has strong ability to explain the COVID-19 epidemic; besides, the companies, transport services, residential communities, restaurants, and community facilities were also the dominant factors in the spread of the epidemic; 4) the combined effect produced by the intersecting factors can increase the explanatory power. The infected migrant factor interacted strongly with the community facility factor with the q value of 0.895. This indicates that the interaction between infected migrants and community facilities played an important role in transmitting COVID-19 at the county level

Fusing attention mechanism with Mask R-CNN for instance segmentation of grape cluster in the field

Funding This work was supported by the National Key R&D Program Project of China (Grant No. 2019YFD1002500), Guangxi Key R&D Program Project (Grant No. Gui Ke AB21076001) and the Shaanxi Provincial Key R&D Program Project (Grant No. 2021NY-041). Acknowledgments We would like to thank Shan Chen, Lijie Song, Shihao Zhang and Qifan Chen for their work on field data collection.Peer reviewedPublisher PD