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Fous
Visual location intelligence for photos
Fous turns photos into location context. Upload an image and Fous estimates where it was taken by analyzing visible scene details, without relying on EXIF, GPS, or embedded metadata. The first preview focuses on San Francisco, with confidence-scored results, map review, and a workflow built for verification, research, investigations, trust and safety, and personal photo archives.
About Fous on Product Hunt
“Visual location intelligence for photos”
Fous was submitted on Product Hunt and earned 12 upvotes and 6 comments, placing #29 on the daily leaderboard. Fous turns photos into location context. Upload an image and Fous estimates where it was taken by analyzing visible scene details, without relying on EXIF, GPS, or embedded metadata. The first preview focuses on San Francisco, with confidence-scored results, map review, and a workflow built for verification, research, investigations, trust and safety, and personal photo archives.
On the analytics side, Fous competes within Artificial Intelligence and Maps — topics that collectively have 482.1k followers on Product Hunt. The dashboard above tracks how Fous performed against the three products that launched closest to it on the same day.
Who hunted Fous?
Fous was hunted by Vojtech Cekal. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Fous including community comment highlights and product details, visit the product overview.
Hey Product Hunt 👋
I’m Vojtech, co-founder and CEO of Fous.
I’ve always found it strange how much location context can be hidden in plain sight. A street sign, a building edge, a storefront, the shape of a road, the way a city looks in the background. Humans can sometimes recognize those clues instantly, but doing it consistently from an image alone is much harder.
Fous is my attempt to make that workflow faster.
Upload a photo, and Fous estimates where it was taken by analyzing visible scene details. It does not rely on EXIF, GPS, or embedded metadata, so it can still work with screenshots, cropped images, video frames, and files where metadata is gone.
The first preview is focused on San Francisco. I chose one city on purpose: better coverage, clearer accuracy targets, and faster iteration before expanding.
What’s included today:
- Photo upload with crop support
- Location estimates from visual scene details
- Confidence-scored results
- Map-based review of possible matches
- No GPS or EXIF required
- A focused San Francisco preview
I think this can be useful anywhere location context matters: verification, research, investigations, trust and safety, journalism, and personal photo archives.
This is still an early preview, so I’m especially interested in the edge cases: images that fail, confusing results, scenes where confidence feels wrong, and workflows where this would be genuinely useful.
Try it here: https://fous.com
I’ll be around all day reading feedback and answering questions.