Start Building GeoAI Skills

You’ll be more marketable if you learn GeoAI

What is GeoAI

Everyone has heard of Artificial Intelligence (AI). It’s the automation of tasks based on the learning of behaviors seen in dynamic data to produce new information.  The algorithms are self learning and continually discover trends.   Add locational prediction and you have GeoAI.

The most common example that you use is vehicle navigation.  Big data is compiled from the behaviors of millions of vehicles.  Programmed algorithms discover patterns in vehicle routing.  These patterns are then conveyed to you as a route that saves you time, gas, and decreases pollution.  GeoAI will advance to a whole new level when communicating speed, fueling, and signage to self-driving vehicles.

AI concepts simply explained

Let’s get a handle on 5 other terms that support the creation of GeoAI. We’ll use the simple example of the industry of selling hamburgers. But the same principles apply to any multivariable problem such as tracking spread of disease or reacting to climate change indicators.

Artificial Neural Networks (ANN)

It’s a network made up of multiple nodes where each node has a function determining a simple output. This emulates the way a biological brain fires decisions in animals.  The ANN is used to train an AI system on how to think quickly.

Machine Learning (ML)

This is a model used for well-defined tasks with structured data.  Outcomes teach the model to make new decisions.

Natural Language Processing (NLP)

This is the machine learning technology that gives computers the ability to interpret, manipulate, and comprehend human language.

Deep Learning (DL)

This is a type of machine learning in which multiple layers of complex tasks can make sense of unstructured data such as posts in the social media sphere.  

Ensemble Learning (EL)

An approach in which two or more models are applied to the same data and the predictions of each model are combined.

GeoAI skills are marketable

The projection is that AI types of jobs will increase by 40% from 2023-2027. And the average salary of a machine learning engineer is $133,226 (365 DataScience report).

There are a combination of technologies that you should be looking to learn:

  • APIs – programmer interfaces that allow you to fetch a variety of data
  • Python – create scripts that automate data extraction, cleaning, and loading
  • R – a package for statistical analysis and graphics
  • Google Cloud / AWS – available preconfigured base models to create content or make predictions
  • ArcGIS Pro – new GeoAI tools within ArcToolBox to run spatial functions

So now it’s time to take action with your portfolio.  You might start by learning from tutorials at Esri to run some of the no-code GeoAI tools.  Or you might seek a college degree that offers a class within their curriculum.  Alternatively, you might want a more practical approach and learn how to create a GeoAI process in a project use-case.  If you pursue this last option, you’ll be exposed to the wider tech stack that’s used in the industry. 

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