Build the Future of
Infrastructure with AI

01

Citizen-to-City AI Bridge

Modern cities run on structured data, but citizens report problems in messy, unstructured ways — tweets, voice memos, emails. Build AI middleware that automatically translates raw citizen reports into auditable, actionable work orders with crew assignment, priority scoring, and a chain-of-thought trust layer.

Pick one challenge to go deep on:

• Crisis Cluster Detector — Group multiple reports in the same area and time window into a single major incident.
• Severity Scorer — Extract high-hazard keywords to auto-escalate priority to Level 1.
• Deduplication Guardrail — Use fuzzy semantic matching to suppress duplicate reports for already-open issues.

Trimble & Colorado Smart Cities Alliance

02

Asset Management Capture Model

State DOTs are deploying mobile mapping systems to capture roadway assets at highway speeds. Using a real ~1-mile LiDAR and imagery dataset captured in Mannford, OK with a Trimble MX9, build an AI/ML model that extracts and classifies key asset categories with geospatial accuracy.

Asset categories to extract & classify:

• Pavement — Surface condition and extent.
• Utilities — Visible utility infrastructure along the corridor.
• Signs — Traffic and regulatory signage with position data.
• Safety Structures — Guardrail, rumble strips, and related elements.

WSB & Trimble