AI Photo Location Finder

Beyond black-box guessing: our explainable AI photo location finder highlights exact visual evidence boxes and cross-verifies against topographic maps.

01Coastal Palm & Art Deco Parapet

02Solar Azimuth 248° · 17:42 EDT

03HVHZ Laminated Balcony Glazing

01 / 03

Ocean Drive District, Miami Beach

25.7826° N, 80.1341° W · ±140m radius

94.8%

match

Solar Timestamp & Azimuth17:42 EDT · 248° WSW

Shadow azimuth (248° WSW) and 14.2° solar elevation lock the capture window to 17:22–17:48 EDT on the South Florida coast.

Horizon Vegetation & Parapet

Sabal palmetto crown silhouette and stepped Art Deco cornice narrow the coastline to South Florida.

Solar Azimuth & Shadow Ratio

Low-angle golden reflection across the deck slats matches 25.7° N latitude pre-sunset geometry.

Window Frame & Glass Spec

Miami-Dade HVHZ laminated balcony rail profile standard in South Beach coastal properties.

Street View

▸ Technical Specifications & Verification FAQ↓

Unlike black-box AI photo location finders that output an unverified city name, LocatePic pairs multimodal vision reasoning with deterministic cartographic constraints—showing you the exact bridge pier, karst cone, or tree species that proves the coordinate.

EXPLAINABLE CARTOGRAPHIC AI VS BLACK-BOX GUESSERS

Metric	Method	Precision	Note
Visual Evidence Bounding Boxes	Semantic Callout Flags ([R-01], [M-02], [S-03])	100% Explainable	Shows exactly which pixels triggered the geographic match
Regional Exclusion Logic	Explicitly States Why Similar Regions Are Ruled Out	Zero Hallucination	Example: Rules out Guilin spire karst vs Qingyuan cone karst
Client-Side EXIF Pre-Filter	Reads Hardware GPS Before Invoking Vision Models	0ms Exact Hit	Saves AI compute credits when native GPS tags exist
Video & Privacy Scrub Suite	Built-In MP4 Frame Capture + EXIF Scrubber	All-in-One Workbench	No need to switch between separate metadata and video tools

How does an AI photo location finder identify places it has never seen before?

Instead of memorizing every street corner on Earth, the model recognizes regional rules: soil color, roof tile geometry, utility pole cross-arms, vegetation belts, and highway guardrail standards.

What does the confidence percentage and error radius (±km) mean?

Confidence reflects consensus across our 5 deduction nodes. When EXIF GPS is intact, error radius is ±0.01 km; when relying purely on natural horizon and flora clues, error radius is reported transparently (e.g., ±1.4 km).

Can I use this for journalistic verification and archival cataloging?

Yes. The workbench is designed specifically for OSINT researchers, journalists, and travel archivists who require auditable deduction steps.