Abstract. Land cover data is one of the principle sources to understand changes of the earth, develop sustainable globe, conduct environment change studies, land source management, and many other societal benefit areas. Globeland30 is the first multi-temporal (for years 2000 and 2010) global land cover datasets at 30-meter resolution. Based on this dataset, an online geo-statistical system has been developed named “GlobeLand30 GSA”. It helps users from different technical expertise backgrounds to visually explore and quantitatively analyse global land cover data. Different from other Web geoprocessing services and applications or desktop GIS software, this system has developed algorithms to correct multi-type errors in computing global land cover statistics, and applied optimal strategies of pre-organizing geography and auxiliary datasets in order to provide rapid response to multiple co-occurrence user-requests. This paper presents the system architecture and the developed data processing strategies, and also the related accuracy and efficiency assessment of GlobeLand30 GSA. The result shows that the system can provide accurate and efficient land cover statistical analysis for various size and shape of user requested areas, and is capable of handling as much as 200 concurrent user-requests with reasonable response time using a low-end desktop computer system environment.
Abstract. Inspired by the immense success of deep neural network in image processing and object recognition, learning-based image super resolution (SR) methods have been highly valued and have become the mainstream direction of super resolution research. Base on the recent proposed state-of-art convolution neural network (CNN) super-resolution methods, this paper proposed a generative adversarial network for single satellite image Super Resolution reconstruction. It built on a trained deep residual network to generate preliminary SR images, combined with a discriminative network learns to differentiate preliminary SR images and High resolution samples. The experiments results show that our method can use existing model parameters to refine SR image performance.
Abstract. In the process of disaster emergency response, how to achieve rapid sharing of geographic information data is still a difficult problem to be solved. Combining the demand for emergency geographic information data in response to emergencies and major natural disasters in China, investigating the current situation of existing emergency geographic information data sharing, and based on the actual emergency geographic information service guarantee work, this paper researches and analyzes the sharing demand, designs the emergency geographic information data sharing model, and studies the emergency geographic information data sharing mechanism from the aspects of sharing platform, data sharing requirements, and service sharing requirements.
Abstract. In the face of the problems that have emerged along with the rapid industrialization and urbanization in China in recent years, the country proposes to compile the national main functional area plan, divides the national land space into four types of main functional areas: optimized development, key development, restricted development and prohibited development, and carries out the compilation of the national main functional area plan at two levels: national and provincial. The spatial evaluation indexes of the country and the results of the main functional area planning are expressed graphically in the form of thematic maps to make them visual, intuitive and easier to understand. In this paper, we study the topic selection of the National Atlas of Main Functional Area Planning, and mainly introduce the research results of the atlas in topic selection research, content structure design and layout design based on the comprehensive analysis of national and provincial information and following the principles of topic selection.
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