AI-Labeling: Annotate pointcloud for AI training

This page is about labeling pointcloud to serve custom AI-training jobs and create detectors to be used in the Analysis Engine.

1. Create a trajectory file that will be used as a canvas to easily navigate through the scene and label all elements of interest
   • Drag & drop your mapping run
   • Create a vector resource for trajectory to constraint navigation
   • Copy trajectory file in installation folder
   • Define trajectory as linear reference and lock it
2. Create an asset class that will be labeled (e.g: pylons)
   • Create asset ressource
   • Pinpoint asset objects in mapping run
3. Select pointcloud to attribute to an asset object
   • Select associated pointcloud and add pointcloud selection to asset objects
   • Measure next asset object until the entire dataset is reviewed
4. Generate consolidated classified pointcloud + assets to serve AI-detector training
   • Generate data and merge pointcloud selections
   • Pointcloud selection to classification
5. Upload classified pointcloud to enable detector training
   • Upload classified OPC to Reality Management
   • Bentley team takes it from here and will start detector training