AI 4 Infra Challenge – Advanced Track

Welcome to the AI 4 Infra Challenge — Advanced Track

AI, along with advances in hardware technology, has greatly lowered the cost to captured infrastructure assets along a roadway using lidar and image capture. The more infrastructure owners are able to capture and extract using this approach, the larger their ROI on investing in the technologies. For this reason many state DOTs are for example deploying these mobile mapping systems on their road networks. Another key reason is that the technology improves safety by capturing information at highway speeds instead of putting humans next to active roadways.

In this challenge, we are asking students to take a section of roadway that was mobile mapped by WSB using the Trimble MX mobile mapping system and develop a model that would allow an infrastructure owner to extract and classify their some of their top assets -

The objective is to extract and classify four asset classes:

• Pavement: The travelled surface and its painted markings
• Utilities: Poles, overhead conductors, cabinets etc...
• Signs: Panels and the structures carrying them etc...
• Safety: Guardrails, barrier, rumble strips etc...

Event Schedule

• Kickoff — Thu, Sept 3, 4:00–6:00 PM, Student Commons 1600
• Submissions open — through Sept 11, 11:00 AM
• Demo Day / Judging — Fri, Sept 11, 11:00 AM–3:30 PM, North Classroom
• Awards Ceremony — Fri, Sept 11, 4:00–5:00 PM, North Classroom 1005
• Industry Symposium (winners showcase) — Wed, Sept 16, 9:00 AM–4:00 PM, Jake Jabs Center

The Technical Mandate: "Leverage Existing AI/ML Tools to Build an Efficient Model for Top Roadway Infrastructure Assets"

Using Trimble's Business Center Software and AI/ML tools for mobile mapping lidar/image data, students are challenged to develop their own model that extracts and classify's these four key assets. Students will have access to the mobile mapping lidar/image data and TBC tools for building their own extraction/classification model.

You will be provided about a 1 mile section of roadway data that has been mapped in Mannford OK using lidar/imagery with the Trimble MX9 mobile mapping system.

Trimble MX9 | Mobile Mapping Systems | Trimble Geospatial

​WSB - Mannford OK Project (3).png​

​WSB - Mannford OK Project (2).png​

Hosted by the AI Student Association at the University of Colorado Denver, in strategic partnership with Colorado Smart Cities Alliance, Trimble, and WSB, and in collaboration with Colorado State University, HDR, and SHPE.