By: Patrick Murn, Content Marketing & Communications Specialist
In describing self-driving cars, both “autonomous” and “automated” are terms used in common vernacular. For most intents and purposes they have the same meaning. The thought process behind the terminology, however, implies that automated vehicles are one thing – components being developed to reduce the number of items that a human must concern themselves with - and autonomous vehicles – those vehicles that can fully function without human involvement.
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By: Paul Braun, Vice President
AI is prevalent in all aspects of our lives from the technology behind voice recognition software such as Siri on your iPhone to algorithms that evaluate your credit score or evaluate recidivism amongst convicts prior to parole hearings. I’ve been doing a lot of thinking lately about geospatial AI and in particular, AI that assists in the creation of geospatial data from imagery, lidar, video, SAR and other modalities. Here are 5 topics on my mind:
1. Geospatial AI is Already Here - And Changing Rapidly
A few years ago, the challenge of identifying a cat or dog in an image was thought to be exceedingly difficult yet within only a few short years, it can now be done using AI with a very high level of success (~98%). As has been often quoted, we are “swimming in sensors and drowning in data” and our industry needs the same ability to extract data from the numerous platforms and sensors at our disposal. A great deal of geospatial AI is developing and we must be aware of it, track its development and use it wisely. Geospatial AI is important for all geospatial professionals whether you are a photogrammetrist, surveyor, engineering, data scientist, CAD technician, database administrator, or GIS Analyst.
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