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Asset Inventory

Control and Optimize Your Right-of-Way Assets

Rather than manually counting and analyzing each right-of-way asset, simply take a video around town and let our model do the busy work.

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Easily Identify Roadway Assets

Automated Detection of Right-of-Way Assets

Leverage advanced AI to automatically detect and catalog assets such as signs, crosswalks, lane striping, and other key features. Our system ensures no critical element is overlooked, creating a comprehensive inventory to support maintenance and planning.

Condition Assessment

Our technology assesses the condition of right-of-way assets in real time, identifying fading, damage, or missing elements. The system offers insights to help you manage and resolve issues effectively, enhancing safety and extending the lifespan of assets.

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Our Deliverables

  PCI Calculations  
Our engineering experts refined the PCI calculations to get the PCI score you need. Whether it’s Paver PCI, StreetSaver PCI score, or our own proprietary Engin.AI PCI, we’ll get you the scores you’re looking for.
  QC Platform  
No need to rate an entire dataset. While our AI model ensures a high accuracy for many detections, we may suggest some images that have a lower probability in accuracy. If there are any detections flagged, our QC platform makes it easy to view and modify any detections that we suggest may need review.
  Comprehensive Data Analysis  
We'll provide tools to evaluate the current inventory and state of your right-of-way assets. Our platform simplifies the process of identifying deficiencies and planning maintenance schedules.
  Reports & Geospatial Modeling  
Get detailed reports tailored to your needs, complete with geospatial maps for easy visualization of asset locations and conditions. Make data-driven decisions with a clear understanding of your assets and where to focus efforts.
** Data must have GPS telemetry for this feature  to be available.
  Fine Tuning  
This optional service allows your data to be the most accurate it can be. While our model is already very robust, ensure greater accuracy when we label and train the model specifically with a greater sample from your own dataset to help them recognize your specific data even better.
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