Drone infrastructure-inspection dataset built in-region

The challenge and pain points

Development cycles stalled waiting for field imagery and consistent defect labels.

• Model development stalled because the previous vendor could not source enough real-world inspection imagery.
• Defect labels were inconsistent, so the same crack or corrosion pattern was labeled differently across the set.
• There was no shared defect taxonomy, so annotators disagreed on what counted as which defect.
• Waiting on field imagery meant training runs paused for data rather than progressing.
• Buying stock imagery did not fit, because inspection defects have to be captured in the field on real infrastructure.

What Corpshore did

Combined field collection with a defect taxonomy and tiered QA to deliver a clean, consistent training set.

1. 1
   Plan the field collection

   Scoped the infrastructure types, defect classes, and capture conditions before sending teams out, so collection targeted the gaps.

2. 2
   Design the defect taxonomy

   Built a defect-specific taxonomy that was unambiguous for annotators, so identical defects are labeled identically.

3. 3
   Collect imagery in the field

   Captured real-world inspection imagery in-region on actual infrastructure, rather than waiting on a platform to source it.

4. 4
   Annotate to the taxonomy

   Labeled the collected imagery against the taxonomy, with the three-tier QA cascade gating each batch for consistency.

5. 5
   Deliver a clean training set

   Handed over a purpose-built, consistently labeled dataset the team could train on without a sourcing bottleneck.

The solution