9/23/2023 0 Comments Parse geojson nasa world wind![]() ![]() Data accessibility is highly optimized using standard formats including internationally certified open standards (W*S). The NASA World Wind visualization platform is open source and therefore lends itself well to being extended to service *any* requirements, be they proprietary and commercial or simply available. The benefits to understanding for information delivered in the context of its 4D virtual reality are extraordinary. NASA World Wind has only one goal, to provide the maximum opportunity for geospatial information to be experienced, be it education, science, research, business, or government. NASA World Wind, Open Source 4D Geospatial Visualization Platform: *.NET & Java* GISpark not only provides spatiotemporal big data processing capacity in the geospatial field, but also provides spatiotemporal computational model and advanced geospatial visualization tools that deals with other domains related with spatial property. The associated geospatial facilities of GISpark in conjunction with the scientific computing environment, exploratory spatial data analysis tools, temporal data management and analysis systems make up a powerful geospatial computing tool. GISpark can also integrated with scientific computing environment (e.g., Anaconda), interactive computing web applications (e.g., Jupyter notebook), and machine learning tools (e.g., TensorFlow/Orange). Within this framework, SuperMap GIScript and various open-source GIS libraries can be integrated into GISpark. Spark-based algorithm framework is developed for efficient parallel computing. The virtual storage systems such as HDFS, Ceph, MongoDB are combined and adopted for spatiotemporal data storage management. OpenStack and Docker are used to build multi-user hosting cloud computing infrastructure for GISpark. GISpark is constructed based on the latest virtualized computing infrastructures and distributed computing architecture. Therefore, we developed GISpark - a geospatial distributed computing platform for processing large-scale vector, raster and stream data. Such challenges require a scalable and efficient architecture that can store, query, analyze, and visualize large-scale spatiotemporal data. Data- and compute- intensive characteristics inherent in geospatial big data increasingly pose great challenges to technologies of data storing, computing and analyzing. Analyzing large amounts of geospatial data can provide great value for both industrial and scientific applications. Geospatial data are growing exponentially because of the proliferation of cost effective and ubiquitous positioning technologies such as global remote-sensing satellites and location-based devices. GISpark: A Geospatial Distributed Computing Platform for Spatiotemporal Big Data ![]()
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