No GPS needed.
No metadata needed.
AI reads the scene.
Identify the location
from a photo
Upload any photo. Orca 2.1 reads the visual evidence (architecture, signage, road design, vegetation, terrain) and returns ranked location candidates with confidence scores. No GPS or metadata required.
When a photo needs an answer
The image exists. The location does not. These are the situations where identifying a place from a photo stops being optional.
A friend insists the photo was taken in Lisbon. You think it looks like Porto. Neither of you has the original file with location data. Upload it, get ranked candidates, settle it in under a minute.
A rental ad shows a street-view photo. The neighborhood description sounds expensive but the image looks wrong. Identify the actual location before you transfer a deposit.
An image surfaces in a news story or on social media with no stated location. Identify where it was taken, cross-reference the candidates, and document the evidence chain.
An old travel photo with no label. No caption. No context. The scene looks familiar but you can not place it. The image still contains everything needed to identify the location.
How Orca 2.1 identifies a location
The model does not search for a matching image. It reads the scene the way a trained analyst would: signal by signal, eliminating regions until a ranked candidate list is the only thing left.
Upload the photo
JPG, PNG, or WebP. Drop it in or paste a URL. No account required to start. If you have a video, pause at a clear outdoor frame and save it as a still. The format is irrelevant. The visual content is what matters.
Visual signals are read in parallel
Orca 2.1 reads dozens of signals simultaneously: architectural period, road marking conventions, language script on signage, vegetation species, terrain type, vehicle plate formats, utility infrastructure, light angle. Each signal assigns probability mass to different parts of the world.
Example: A photo shows white lane markings, right-hand traffic, timber-frame houses with dark roofs, and a sign in what appears to be Scandinavian. White lane markings narrow to Europe. Right-hand traffic eliminates the UK. Timber-frame construction and the script point to Norway or Sweden. The model narrows continuously until the candidate list converges.
Ranked candidates with evidence
The result is not a single pin. It is a ranked list of candidates, each with a confidence score and the evidence chain that produced it. You can see which signals pointed where, and you can compare each candidate against Street View imagery before deciding. The model narrows the world. You make the final call.
What you get back
Every identification returns a structured evidence bundle, not just a coordinate. Each layer is designed to help you verify the result, not just accept it.
Ranked candidates
Multiple location candidates in order of likelihood, not a single guess.
Confidence scores
Each candidate carries a score reflecting the strength of the visual evidence.
Evidence chain
The specific signals that support each candidate: what the model read and why.
Street View verification
Side-by-side comparison with Street View imagery so you can confirm the match yourself.
Contradictions flagged
Signals that don't fit a candidate are surfaced, not suppressed. Honesty over false confidence.
Watch signals build a location
This is a simplified view of what happens when you upload a photo. Each signal read narrows the world a little more until a location emerges.
Reading visual signals...
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The real analysis reads many more signals in parallel and returns a full evidence bundle. This shows the structure, not the depth. Try it on your own photo.
The image is enough
Every major social platform strips EXIF and GPS data from photos on upload. Instagram does it. WhatsApp does it. Twitter/X does it. Screenshots never had it. Tools that depend on metadata fail the moment metadata disappears.
Oceanir does not use metadata. The identification is based entirely on the visual content of the image: what is visible in the pixels, not what was recorded in the file header. The place is always in the photo. The model has learned to read it.
This means Oceanir can identify locations in screenshots, re-compressed images, photos forwarded through messaging apps, and any image where metadata was never present or has been removed.
Identify the location now
Free to try. No credit card. Upload a photo and get a ranked location identification in under 30 seconds.
Upload a photoCommon questions
Yes. Oceanir's Orca 2.1 model reads the visual content of a photo to identify where it was taken. It analyzes architecture, road markings, signage, vegetation, terrain, and other geographic signals embedded in the pixels. No GPS or location metadata is needed.
Orca 2.1 achieves 20.1% accuracy at 1 km and 46.8% at 25 km on the Im2GPS3k benchmark, the highest published scores among visual geolocation systems. Outdoor scenes with distinctive buildings, signage, or terrain produce the strongest results. Accuracy reflects the visual richness of the image, not the resolution.
Yes, and that is precisely the point. Social platforms (Instagram, WhatsApp, Twitter/X) strip EXIF and GPS data on upload. Oceanir performs pure visual analysis: it never reads metadata because the image's visual content is the only input it needs.
Orca 2.1 reads dozens of geographic signals in parallel: road marking conventions, curb profiles, utility pole styles, architectural periods, facade materials, language on signage, vehicle plate formats, vegetation species, terrain type, shadow angles, and sky color temperature. Each signal narrows the candidate set until a ranked list of locations emerges.
Indoor scenes without exterior context are harder, but not always impossible. If a window is visible, the exterior view (architecture, terrain, street) carries geographic signal. Distinctive interior design, signage in a specific language, or branded elements can also provide partial clues. Outdoor scenes remain the most reliable.
Free to try, no credit card required. Each analysis uses one credit. Paid plans include Pro for individual verification, Developer for API access, Unit for shared workspaces, and sales-led Agency and Enterprise plans.
Oceanir is a privacy-first platform. Uploaded images are used solely to run the analysis you requested. They are not indexed, shared with third parties, or used to train models. You can delete your analysis history from Settings at any time.