"Paving" the Way for Smarter Communities Using AI

If you had asked me what I’d be doing with GIS when I declared it as my minor at the Quinney College of Natural Resources at Utah State University, I probably would have said I was going to be a National Park Service (NPS) ranger. Actually, now that I think about it, not just probably—I vividly recall telling my family that at a dinner in 2017. Could you imagine anything better than traveling through Yosemite, Joshua Tree, and Katmai, home of my favorite fat bear, Holly? ?

Today, I’m obviously not working at any National Parks and I have yet to visit Holly in Katmai, but I will soon. I did, however, find a fascination with the intersection of GIS and local governments. By designing/supporting applications that make it easier for Public Works crews to identify utilities in the ground when they have to fix a water main break in the middle of the night, I can create real, tangible impacts for both the workers and the residents they serve. Providing recommendations for updated political boundaries based on the latest census data means a more equitable say in new businesses or preserving open spaces. But wait, it gets better!?

Then I learned about... PAVEMENT! You were probably expecting something different, but seriously, think about it. How did you get to work today? How did you drop your kids off at baseball practice? How do you get to Trader Joe’s? Roads are?the veins of a city, facilitating travel to the places you love. Municipalities are responsible for deciding which roads should and shouldn’t be worked on each year based on factors including function, level of importance, condition, how they overlap with capital improvement projects, and more. There are people in most local governments, generally an engineer or streets foreman, making decisions about how to use your tax dollars most effectively, guarantee reliable transportation.?

Like all sectors, this science of deciding where to allocate resources is getting better with the use of light detection and ranging (LIDAR) and AI (there’s that buzzword again). More specifically, the types of AI involved are computer vision, machine learning, and deep learning. So, let’s break down what these terms mean for pavement.?

LIDAR Data Processing:?

LIDAR uses lasers to create detailed 3D maps of roads. The AI processes this data to understand the road's surface.?

Computer Vision and Image Analysis:?

The AI looks at the LIDAR data like it would look at pictures, spotting patterns and features that indicate different road problems.?

Machine Learning and Deep Learning:?

The AI uses advanced learning techniques to recognize and classify road issues. It learns from examples to identify the types and severity of road damage accurately.?

This combination of techniques provides municipal staff with detailed insights into road conditions, which are crucial for maintenance and planning purposes. This is a breakthrough in traditional manual collection methods and that is why I am changing my title to "Asphalt Aficionado" or maybe "Roadway Raconteur."?

Okay, if you’ve made it this far, I appreciate you, and I hope I’m explaining this in a way that anyone could understand because AI should not be scary like Terminator. People are afraid of what they don’t understand, like the deepest depths of the oceans, but I’m confident that if we stop talking about AI as if it’s only for the most elite scientists, more people will feel comfortable using it in everyday business processes like pavement management.?

Pavement is sweet, and if I could go back in time to tell my younger self that we would be working at the crossroads of AI, GIS, local government, and pavement management, I’m not sure my younger self would have gone this route... good thing time travel isn’t possible - yet.?

If you’re responsible for pavement management, reach out to me and let’s talk about the future of keeping our communities flowing!?

“AI is likely to be the best or worst thing to happen to humanity." -Stephen Hawking?