AI Photo Geolocation

Perceived Accuracy and Behavior

• Reported performance is highly mixed. Some users see “scary accurate” results, even down to specific hiking trails, island identification, or interiors roughly localized by regional architecture.
• Others report large errors: wrong countries or cities, misplacing NYC as Detroit, Chicago as NYC, Montevideo as Buenos Aires, Turks and Caicos as the Bahamas, etc.
• It seems stronger on famous landmarks and highly photographed tourist spots, weaker on generic streets, interiors, and rural or nondescript landscapes.
• Distance errors range from “same city, wrong building” to “wrong continent,” though sometimes it’s directionally close (e.g., off by a few hundred kilometers or neighboring country).

Hallucinations and Explanations

• Explanatory text often mentions non‑existent features: houses in lake photos, palm trees in indoor scenes, signs and cowboy hats that aren’t there, or “English road signs” where no signs exist.
• Some descriptions are eerily specific and correct (e.g., identifying a fire tower or helicopter), but many are generic stories stitched to probabilistic guesses.

Biases and Limitations

• Strong bias toward the US and certain regions; many non‑US photos are mislocated to US cities or to major hubs like Moscow.
• Users note it can be fooled by architectural style (e.g., Venetian or French style misplacing Italian locations).
• Some question whether EXIF data is used; others report stripping metadata and still seeing good/bad performance, so this remains unclear.

Technical Implementation Speculation

• Several commenters infer a visual-embedding + nearest‑neighbors approach, potentially combined with a multimodal model and an LLM for explanations.
• Others argue a simpler classifier might be more appropriate than an LLM, given the hallucinations.

UX and Reliability Issues

• Many report the web app as “broken”: heavy flickering, lockups, constant errors, expired API keys, especially on Firefox/Linux and some Chrome setups.

Ethical and Privacy Concerns

• Strong worries about doxxing, OSINT misuse, and normalizing tools that circumvent intentional metadata removal.
• A serious issue surfaced: user uploads were stored in a Google bucket that was publicly accessible, and the same bucket reportedly contained scraped dating-app images, raising data-handling and consent concerns.