No GPS. No metadata. AI reads the scene and estimates where it was taken. Upload a photo and get a result in 30 seconds. Free, no signup.
Oceanir is free to try in the browser with no credit card. Starter is $10 per month for 30 credits (D1 and D2). Pro is $29 per month for 100 credits, D3 forensic depth, Street View verification, and API access. Enterprise starts at $1,499 per month for teams and pooled credits. Full plans: https://oceanir.ai/pricing
Yes. OSINT analysts use Oceanir to geolocate photos and video frames when EXIF and GPS are missing. Upload the image; the model returns ranked coordinates, a confidence score, and the visual cues it used — signage, architecture, plates, terrain — typically in under 30 seconds. Same workflow for journalists and claims investigators. Walkthrough: https://oceanir.ai/osint-image-geolocation
No GPS needed.
No metadata needed.
AI reads the scene.
You have a photo. No GPS. No metadata. Just pixels. Oceanir reads the scene (roads, signs, buildings, terrain) and estimates where it was taken. No EXIF data required.
Nobody wakes up wanting to geolocate a photo. Something happened first. A question that won't go away. An image that needs an answer.
A video surfaces on social media claiming to show a protest in a specific city. The account is anonymous. The metadata is gone. You need to verify where it was actually filmed before you publish.
A missing person's last known communication was a photo sent through a messaging app. The app stripped the GPS data. The image is all you have.
You found an old photo in an archive with no location label. The buildings, the road markings, the vegetation. They all say something about where this was taken. You need to hear it.
Someone shared a photo and you want to know where it is. Not because it matters. Because you can't stop looking at it and wondering.
In every case, the metadata is gone. Instagram strips it. WhatsApp strips it. Twitter strips it. Screenshots never had it. What's left is the image itself, and the image is enough.
The process is simple. The thinking behind it is not.
Any JPG, PNG, or WebP. Drag and drop. No account needed to start. You can also extract a still frame from a video. Pause at a clear outdoor shot, save the frame, upload it. The format doesn't matter. The pixels do.
This is not image matching. The model doesn't compare your photo against a database of known locations. It reads the image the way a trained geographer would: identifying the road marking style, the architectural period, the tree species, the shadow angle. Each clue narrows the world.
Example: A photo shows yellow license plates, left-hand traffic, and terraced brick houses with sash windows. Yellow plates narrow it to the EU and China. Left-hand traffic eliminates China. Sash windows and brick terraces point to the UK. The model doesn't guess. It eliminates.
Oceanir returns ranked candidates, not a single point on a map. Each candidate comes with a confidence score, the evidence chain that supports it, and a Street View comparison so you can see for yourself. You make the final call. The model narrows the world. You close the case.
Most photos shared on social media (Instagram, Twitter/X, Facebook, WhatsApp, Signal) have their EXIF and GPS metadata stripped automatically on upload. That means traditional metadata-based tools return nothing.
Oceanir does not need EXIF data. The AI performs pure visual analysis: reading architectural styles, road markings, signage languages, terrain features, vegetation types, and hundreds of other geographic signals embedded in the pixels themselves.
This means Oceanir can estimate the location of screenshots, re-saved images, photos from messaging apps, and any image where metadata has been lost or was never present.
When you upload a photo, Oceanir doesn't search for a match. It reads the image signal by signal. Each clue narrows the world a little more. Here's what that looks like.
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Google Lens is good. It can identify landmarks and match visually similar images. But some images it simply won't locate, and those tend to be the images people actually need help with. Here's where the difference shows up.
A photo from WhatsApp with metadata stripped
Nothing. No GPS, no result.
Reads the scene. Buildings, road style, vegetation. Gets a location.
A screenshot of a Google Street View frame
Can't match it. It's a screenshot, not the original image.
Reads the visual content in the screenshot the same way it reads other scene media.
A generic suburban road with no landmarks
Too generic. No distinctive features to match against.
Road markings, curb profiles, utility pole style, driveway spacing. All geographic signals.
A photo re-saved multiple times, compressed to hell
Quality too low for matching. Returns nothing.
Compression destroys metadata, not geography. The road is still there. The buildings are still there.
An indoor photo through a window
Indoor = no match. Period.
What's visible through the window? Architecture, sky, terrain. Partial clues still count.
The pattern is the same: other tools depend on finding a visual match in their index. When there's no match (because the image is too generic, too compressed, or too stripped of metadata) they return nothing. Oceanir reads the geography directly from the pixels. No match required.
Free to try. No credit card. Upload a photo and get results in under 30 seconds.
Upload your photoYes. Oceanir does not rely on EXIF or GPS metadata. The AI reads visual clues in the image itself (roads, signs, buildings, terrain, vegetation) to estimate the location. Most photos shared on social media have metadata stripped, so Oceanir is designed to work without it. Free to try, no credit card.
Oceanir's Orca 1.5 model achieves 32.2% accuracy at 1 km and 64.3% at 25 km on the Im2GPS3k benchmark, the highest published scores among visual geolocation systems. Accuracy depends on the visual clues available. Outdoor scenes with distinctive architecture, signage, or terrain produce the best results.
Outdoor scenes with visible roads, street signs, storefronts, building facades, or distinctive terrain work best. Indoor photos, nighttime shots, heavily cropped images, and close-up portraits are harder because they contain fewer geographic clues.
Free to try, no credit card required. Each analysis uses one credit. API access is included with Pro; Enterprise access on request. Paid plans include Starter for occasional D3, Pro for individual verification workflows, and sales-led Enterprise plans.
Yes. Extract a still frame from your video (a clear outdoor shot works best) and upload it as a JPG, PNG, or WebP. Oceanir analyzes the frame the same way it analyzes other scene media.
Oceanir is free to try in the browser with no credit card. Starter is $10 per month for 30 credits (D1 and D2). Pro is $29 per month for 100 credits, D3 forensic depth, Street View verification, and API access. Enterprise starts at $1,499 per month for teams and pooled credits. Full plans: https://oceanir.ai/pricing
Yes. OSINT analysts use Oceanir to geolocate photos and video frames when EXIF and GPS are missing. Upload the image; the model returns ranked coordinates, a confidence score, and the visual cues it used — signage, architecture, plates, terrain — typically in under 30 seconds. Same workflow for journalists and claims investigators. Walkthrough: https://oceanir.ai/osint-image-geolocation