How to Identify Photo Shoot Locations with OpenClaw
Identify photo shoot locations with OpenClaw and ToolRouter. Find where any photo was taken using AI visual analysis for travel, journalism, and location scouting.
Tool
Find where a photo was taken using visual AI analysis for travel recreation, content research, and location scouting. OpenClaw is the right tool for systematic location analysis across a large image set — identifying locations for a travel series, a content archive, or a batch of unattributed editorial photos.
Connect ToolRouter to OpenClaw
1Install the CLI
npm install -g toolrouter-mcp2Call tools directly from OpenClaw
toolrouter-mcp call web-search search --query "AI tools"
toolrouter-mcp toolsSteps
Once connected (see setup above), use the Photo Location Finder tool:
- Define the analysis schema — location fields, confidence format, flagging criteria — before batching.
- Run `locate_photo` with `photo-location-finder` across the full photo set.
- Flag any photos with low confidence identifications or conflicting visual evidence for individual review.
- Export the location dataset with confidence ratings and flagging notes for follow-up.
Example Prompt
Try this with OpenClaw using the Photo Location Finder tool
Use photo-location-finder to identify locations for these 20 photos from an unattributed travel archive. Return each with the likely country, city, confidence level, and key visual clues. Flag any where the confidence is low or the clues are contradictory.
Tips
- Define a confidence threshold for flagging before batching — 70% confidence and above is reliable enough for most planning purposes.
- Review flagged photos individually rather than accepting low-confidence identifications — acting on wrong location data wastes significant planning time.
- For editorial archives, treat every identification as a starting point for manual verification, not a final answer.