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By: Patrick Murn, Content Marketing & Communications SpecialistIn describing self-driving cars, both "autonomous" and "automated" are terms used in common vernacular. For most intents and purposes...
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​Two significant challenges face the GEOINT community today; 1) give geospatial context to regional or global activities that allow an analyst to better evaluate a Key Intelligence Question, and 2) ma...
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 Seems like a bit of an odd question but it's a good one. We need to be asking it more often. At the heart of it is a common concern - what needs and questions can be appropriately answered with ...

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UAS

5 Thoughts on Geospatial Artificial Intelligence

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 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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Auditing Geospatial Artificial Intelligence

 By: Paul Braun, Vice President Business Development

The geospatial industry is a prime example of how artificial intelligence can be leveraged to improve its products and services. Not all AI is created equally, however, and that could be an issue. 

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Is it Autonomous or Automated? Phrasing is Important

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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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Event Monitoring and Global Foundation Data Updates from Non-Traditional Sources

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​Two significant challenges face the GEOINT community today; 1) give geospatial context to regional or global activities that allow an analyst to better evaluate a Key Intelligence Question, and 2) maintain global foundation data that meets the needs of end users.

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Driving With Augmented Reality

ionroad

​At first blush, combining the real world with the virtual world while driving sounds like a bad idea. It's bad enough that we can be distracted by the radio or my smart phones let along more technology. However, if used correctly, there is room for augmented reality in the transportation/driving space particularly as autonomous vehicles work their way into common practice.

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6 Tips for Multilingual Development

StrongLinks

As a diverse surveying and mapping company, Continental Mapping uses a large variety of software products in its production workflows, as do many architecture and engineering firms. These include Bentley MicroStation, AutoDesk AutoCAD, BAE Systems Socet Set, DAT/EM Summit Evolution, Esri ArcGIS, QGIS, and Certainty 3D TopoDOT. Many of these applications allow extensions to their toolsets by providing the user with the ability to create custom tools or scripts. This helps automate repetitive workflow steps or expand the functionality to address new challenges thus saving time and increasing the quality of the data being produced. 

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How Fit is Your GIS Data?

Humvee_in_water

 Seems like a bit of an odd question but it's a good one. We need to be asking it more often. At the heart of it is a common concern - what needs and questions can be appropriately answered with a given GIS dataset. The things we map change. A small stream today can be a sizable river in the rainy season. Today's GIS community wants to build and use "live maps" that address these dynamic issues as opposed to "dead maps". To complicate matters, we are looking to to inject Volunteered Geographic Information (VGI), citizen science data or sensor content from the Internet of Things (IoT) into our enterprise geospatial systems. This requires us to evaluate how good an open source data set is in comparison to our base information prior to ingesting it into our enterprise GIS. What it comes down to is that we have no way to quantitatively measure quality, until now.

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