In the geospatial industry, the term “interoperability” has become a bit of a buzz word over the last several years. Interoperability is very simply defined as the ability of diverse systems to work together. At a high level, I consider systems to be interoperable when they play nicely with each other. The nicer these systems play, the less work is required by users to move back and forth between systems. In the world of GIS, this generally means data that is created in one geospatial environment can be quickly pushed to another. This allows users to leverage the capabilities of multiple geospatial platforms in order to take advantage of the known capabilities of each.
As GIS has grown, the need for interoperability has pretty much been universally understood. GIS technology is multidisciplinary by nature. The power of GIS lies in its ability to pull information from many sources together to illustrate connections, relationships, and patterns that might not be obvious in any single data set. This fusion of data enables organizations to make better decisions based on all relevant factors. This process has become increasingly more complex as data sources have multiplied and geospatial software providers face an ever-increasing number of data types to support. To address this issue the geospatial industry has evolved a set of concepts, standards, and technologies for implementing GIS interoperability. This has proven highly beneficial for the geospatial industry as a whole because it has allowed for the integration of data between organizations and across applications and industries.
For GIS users, the increased availability of data collected by remote sensing platforms has promoted the utility of imagery from basic contextual backdrops to new sources of rich geographic information from which to create foundational data layers. This sea change in the use of remotely-sensed data in GIS has been helped along by technological advancements to remote sensing software tools that have consolidated spectral science and raster analysis methods into higher-level, solutions-based tools.
Specialized image analysis tools, like ENVI, provide GIS-related capabilities for creating, editing, and exporting valuable data to a GIS environment. Through a partnership with Esri, Exelis VIS has made steps towards moving beyond software solutions that are merely interoperable with Esri’s ArcGIS platform. Exelis VIS has worked towards a level of integration that makes the process of extracting useable data from remotely-sensed sources and pushing the data to the ArcGIS platform virtually seamless. This level of integration includes the ability to create data in ENVI and send it directly to ArcGIS Desktop, or even drag-and-drop data directly into ArcGIS Online. Exelis VIS has also created a suite of analysis tools and workflows that can be accessed directly through ArcToolbox, so the capabilities of both software packages are available through the same interface.
This may not seem like that big of a deal, but to me it seems quite amazing how far this technology has come in the past several years. With the growth of the cloud and the introduction of web-based platforms such as ArcGIS Online, I suspect the integration of powerful tools from a variety of sources into an easily-customizable GIS environment that suits the specific needs of the user to continue on its current trajectory.