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SBIR/STTR

GIS Function Coupling for Virtual Globes, Phase I

Completed Technology Project

Project Introduction

GIS Function Coupling for Virtual Globes, Phase I
Virtual Globe (VG) systems such as Google Earth, NASA World Winds and Microsoft Virtual Earth provide captivating animated 3D visualizations and support user queries for information at a point. NASA MSFC's VG-based Real Time Mission Monitor (RTMM) enhances management and tracking of field experiment missions. The National Weather Service's RIDGE service uses VG to disseminate radar and support decision assistance. Simpson Weather Associate's Doppler Wind Lidar uses VG technology provided by WxAnalyst to manage field experiment instrumentation and data acquisition in flight. WxAnalyst has recently prototyped the WxAzygyTM Interface to couple external applications with Google Earth (GE). Such user applications are inherently unlimited, and can embrace Geographic Information System (GIS) by inclusion of licensed GIS or the OGC GeoTools open source. Full GIS coupling through a transparent and overlaid interface would provide a standard means for complex user operations in the VG environment. The independence of this interface decouples external functions from the VG, can provide security/privacy where needed, and could potentially encourage VG evolution. Our vision for GIS-VG coupling involves the concept of a "focus object" which is mutually shared by the VG and Interface. This focus object is described in GE by KML 2.2. GE interaction is currently supported through an Application Programmer Interface (API) downloaded with each installation. The GE API could become the basis for a standard and be potentially extended. Possible capabilities in situ with VG include spatial data selection and cross referencing, comparison and cross-correlation of simultaneous and collocated data objects with disparate geometries, and interaction with data servers to acquire, load and subset data "on the fly". This type of new technology will enable greater utilization of extremely large, complicated, and highly distributed datasets on all spatial scales over large geographic areas. More »

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