The object detection of unmanned aerial vehicle (UAV) images has widespread applications in numerous fields; however, the complex background, diverse scales, and uneven distribution of objects in UAV images make object detection a challenging task. This study proposes a convolution neural network (CNN)-transformer hybrid model to achieve efficient object detection in UAV images, which has three advantages that contribute to improving the object detection performance. First, the efficient and effective cross-shaped window (CSWin) transformer can be used as a backbone to obtain image features at different levels, and the obtained features can be input into the feature pyramid network (FPN) to achieve multi-scale representation, which will contribute to multi-scale object detection. Second, a hybrid patch embedding module is constructed to extract and utilize low-level information such as the edges and corners of the image. Finally, a slicing-based inference method is constructed to fuse the inference results of the original image and sliced images, which will improve the small object detection accuracy without modifying the original network. Experimental results on public datasets illustrate that the proposed method can improve the performance more effectively than several popular and state-of-the-art object detection methods.

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The development, deployment, and maintenance of the current space situational awareness (SSA) information system have become increasingly complex. However, researchers cannot flexibly and conveniently apply the research results to practical applications due to the lack of basic research platforms for SSA. Inspired by X as a Service (XaaS), we propose the microservice-based platform for SSA data analytics to provide a scaffold-like platform for researchers. Based on microservice, the architecture for this platform is proposed to meet the requirements of flexible development and loosely coupled deployment. To facilitate the use of the platform, the hybrid data service layer is established to provide basic data for research and the functional service layer is designed to provide services for clients and applications. Due to the massive data processing requirements, the data analysis architecture and processing model, which can easily integrate various user-defined algorithms and significantly improve the computational efficiency, are proposed based on the Lambda architecture. To verify the platform's effectiveness, two cases are established and implemented. The results show that this platform can provide a convenient, flexible, and efficient platform for the requirements of algorithm integration, experiment, and data display from users and researchers.

To effectively guarantee the safety and efficiency of space activities, and to meet the needs of different levels of users for space object situation (SOS) knowledge, formalized and standardized SOS knowledge is needed to assist cognition. A knowledge graph (KG), as a suitable technology that can formally express knowledge and construct a standardized knowledge base, can provide knowledge generation, representation and intelligent service of SOS. In this paper, an SOS information service based on the KG (SOSKG) was constructed. Aimed at different scales of components in SOS, a multi-granularity knowledge structure was constructed, and a formalized expression of SOSKG was realized. To incorporate the complex relations into the SOSKG, a multi-level semantic relation parsing model and formalized representation were proposed from the three aspects of basic relations, spatial relations, and temporal relations. Additionally, a multielement knowledge construction model was constructed to address the dynamic characteristics of various relations in SOSKG under a complex spatio-temporal environment. This study constructed different cases and scenarios to test the service, and the results show that the proposed service can effectively organize and express SOS knowledge in a complex spatio-temporal environment, provide an intermediate bridge between users and SOS knowledge, and promote users' cognition of SOS knowledge.

The implementation of increased space exploration missions reduces the distance between human beings and outer space. Although it is impossible for everyone to enter the remote outer space, virtual environments could provide computer-based digital spaces that we can observe, participate in, and experience. In this study, Sino-InSpace, a digital simulation platform, was developed to support the construction of virtual space environments. The input data are divided into two types, the environment element and the entity object, that are then supported by the unified time-space datum. The platform adopted the pyramid model and octree index to preprocess the geographic and space environment data, which ensured the efficiency of data loading and browsing. To describe objects perfectly, they were abstracted and modeled based on four aspects including attributes, ephemeris, geometry, and behavior. Then, the platform performed the organization of a visual scenario based on logical modeling and data modeling; in addition, it ensured smooth and flexible visual scenario displays using efficient data and rendering engines. Multilevel modes (application directly, visualization development, and scientific analysis) were designed to support multilevel applications for users from different grades and fields. Each mode provided representative case studies, which also demonstrated the capabilities of the platform for data integration, visualization, process deduction, and auxiliary analysis. Finally, a user study with human participants was conducted from multiple views (usability, user acceptance, presence, and software design). The results indicate that Sino-InSpace performs well in simulation for virtual space environments, while a virtual reality setup is beneficial for promoting the experience.deep space exploration is also noteworthy [3]. The Chang'E lunar exploration program, which involves achieving lunar orbit, soft-landing on the moon, and returning a lunar sample has now entered its third stage [4]. The first Chinese Mars exploration mission has also been formally approved, and a Mars probe is planned for launch in the second half of 2020. With the possible opportunities and new avenues that space exploration promises, especially considering the problems of environmental pollution and resource depletion, many countries have set up ambitious space programs [5][6][7]. For human beings, outer space is a relatively "new world" and difficult to reach currently. However, virtual environments, initially viewed as "mirror worlds", can supply computer-based digital spaces that we observe, participate in, and experience in person [8]. With the increasing scope and scale of human space activities, it is essential to construct a digital simulation platform for virtual space environments (VSEs).The traditional geographical information disciplines primarily study the Earth's surface including commercial (e.g., ArcGIS, MapInfo, and SuperMap) or open source geographic information system (GIS) platforms [9]; these platforms focus on pro...