Harris Geospatial Solutions' work in analytics can be traced back to 1977, when David Stern developed Interactive Data Language (IDL) at the University of Colorado to support the Mariner Mars 7 and 9 space probes. That same year, Stern incorporated Research Systems Institute (RSI), and commercially released IDL.
In 1991, RSI released the first version of ENVI, an advanced hyperspectral image analysis software package that was written in IDL. Since those early beginnings, Harris Geospatial has stayed true to its origins of relying on scientifically-proven analytics to create industry leading solutions that extract information from all types of remotely sensed data.
Remotely sensed data is used to make critical decisions, to make discoveries, and to better understand the world around us. Harris has a legacy in developing the sensors that collect virtually every type of remotely sensed data used by commercial organizations and governments across the world.
From our own Geiger-mode LiDAR sensor and CorvusEye Wide Area Motion Imagery (WAMI) sensor, to developing the high-resolution camera systems used in satellites, Harris is recognized as the undisputed leader for developing imaging payloads and is uniquely qualified to create solutions to solve even the most challenging problems.
Harris Corporation is a Principal Member of The OGC (Open Geospatial Consortium), an international not for profit organization committed to making quality open standards for the global geospatial community. These standards are made through a consensus process and are freely available for anyone to use to improve sharing of the world's geospatial data. OGC standards are used in a wide variety of domains including environment, defense, health, agriculture, meteorology, sustainable development and many more.
At Harris we looked at AI, and more specifically Deep Learning as an enabling technology to help solve real-world customer problems. To that end, we have developed a highly-tuned process, relying less on the volume of label data and more on reliable training models and high-performance computing, that allows us to solve those real-world problems quicker and more cost effectively than ever before.
Harris has made multi-million dollar investments in machine learning research and technology for more than five years, putting us at the forefront of this technology. Harris’ machine learning technologies excel at automated target detection, landcover classification mapping, and automated scene state detection. For automated object recognition we're obtaining >>95 percent accuracy on Pan, RGB, MSI, HSI, SAR, LiDAR, and derived point cloud data sets.
Every day, hand held devices, sensors, and satellites create terabytes of location based data content — content that informs and changes the way we map the world. At Harris, we develop the solutions that make it easier and more profitable for our customers to access, manage, and share all this geospatial content.
As geospatial content is consolidated and unified, Harris technology enables users to anticipate future enrichment, enhancement, and maintenance of the content in support of planned and unplanned missions.