[HN Gopher] AI Photo Geolocation
___________________________________________________________________
AI Photo Geolocation
Author : hubraumhugo
Score : 118 points
Date : 2024-05-02 04:41 UTC (18 hours ago)
(HTM) web link (geospy.ai)
(TXT) w3m dump (geospy.ai)
| voidUpdate wrote:
| I'm not convinced by the quality of this. I took some screenshots
| of street view, not including any icons, and it identified them
| as completely the wrong city. One of them included the name of
| the town on a bus stop, which it completely failed to identify,
| placing the picture across the county, also asserting that it
| contained featured that it definitely didn't, such as thatched
| rooves (all rooves in the image were normal slate). I would make
| trust it to get me in the correct area of the country, but that's
| about it
| voidUpdate wrote:
| After a bit more testing, it could successfully identify
| Buckingham Palace and St Michaels Mount (however the location
| wasn't great), however a street overlooking a beach in Cornwall
| was marked as Wales, apparently including house numbers in
| Welsh and English (despite Welsh using English numerals). It
| seems to work somewhat ok if there is a clear image of an
| obvious and distinctive monument, otherwise it isn't
| particularly accurate
| tapland wrote:
| I got similar results, and that would be ok if it didn't sound
| so confident with the guess.
| boesboes wrote:
| It's not very accurate, but it seems consistent. However it quite
| often tells me 'this is in X because the language on the sign'
| when there are no signs at all. Or just now I got 'The house in
| the background is made of wood, which is a common building
| material in Finland.' with a photo of a lake. There is no house,
| there are trees though :)
| lkramer wrote:
| The page is very broken for me (Firefox in Linux), locking up,
| flickering.
|
| I did manage to get it to place a picture of a praying mantis I
| took in Japan to be from California...
| eru wrote:
| I get the same flickering in Firefox on MacOS, but it managed
| to recognise my picture.
| lordswork wrote:
| I got "an error has occurred"
| ape4 wrote:
| Me too - maybe its overloaded
| mhuffman wrote:
| Same, Firefox in linux, also tried with all extensions disabled
| and same thing. Just flickering with an "error" modal.
| sanxiyn wrote:
| This correctly identifies South Korean landmarks, like Diamond
| Bridge in Busan. Since I don't have encyclopedic knowledge of
| world landmarks -- I wouldn't be able to recognize Diamond
| Bridge-like landmarks in United States -- and nearly no one does
| either, that alone is quite useful.
| tgv wrote:
| Doxxing for dough. The ethics committee is out to lunch.
|
| See this thread: https://news.ycombinator.com/item?id=40233248
| ToucanLoucan wrote:
| In their defense, the AI hype folks have been ignoring the
| ethics community from jump. I'd leave too.
| sebzim4500 wrote:
| Pretty cool. Correctly identifies different islands in the
| Galapagos based on the ground and the plants.
| elsadek wrote:
| Childish AI, I gave him two photos, both was totally wrong and
| hundred thousands miles from actual locations.
|
| Don't recommend.
| eru wrote:
| > I'm sorry but GeoSpy is not allowed to process this image.
| Please try again with a different image or contact support at
| info@graylark.io
|
| That was with an image I took in London on my phone.
| bambax wrote:
| This is what happens when I try to scroll to read the results...?
|
| https://i.imgur.com/ywc1Hn0.png
|
| (Chrome on Windows)
| coumbaya wrote:
| My backyard: Germany because there are trees and a fence (? Also,
| no). A picture of the farmer's market of my town: correctly
| assume France but confidently incorrect on the town and landmarks
| (off by 200-300km I'd say).
| mufty wrote:
| I'm not sure how this works under the hood. My initial
| observation is it does not work.
| trebligdivad wrote:
| Hmm interesting; one hit, one probably in vaguely the right area;
| both from scans of ~40 year old photos. (As someone else noted,
| site is rather brokne on Firefox/Linux but does work).
| amarcheschi wrote:
| I put an image of villa isnard, cascina, Pisa, and it was
| recognized as being from France. It is a villa with French
| architecture and olive trees. I then tried to upload an image
| still from Pisa with a building in venetian Gothic style and it
| was recognized being in Venice. It can be deceived quite easily
| imho, it looks like it just search for the corresponding
| architecture (maybe?) and details surrounding it but it doesn't
| search online. Villa isnard is quite famous (at least, you have
| results online) and a Google lens search would have found it
| j-bos wrote:
| Yeah, I would guess it's identifying elements in the photo,
| qualifying the likelihood of those combined elements in a
| particular location, then outputting the assumption. I posted a
| picture of a Texas lake seen from a privatr residence, and it
| correctly guessed Texas, but pushed it off by a couple hundred
| miles into an Austin golf course.
| sanxiyn wrote:
| It seems to use many signals, at least according to its own
| explanation. For example it looks at road signs and license
| plates to identify countries.
| axegon_ wrote:
| Assuming this is more of a proof of concept/prototype, that's not
| bad. It didn't get it[1] right, but at it's core, the guess is
| not terrible, shift it 560km[2] south-east and you'd be bang on.
| I'll admit, I did set the bar a bit high.
|
| [1] https://imgur.com/a/67t0TVt
|
| [2] https://imgur.com/a/aspf8px
| pt_PT_guy wrote:
| Is the website glitchy on firefox?
| DominoTree wrote:
| Very - never seen anything explode quite like it
| wongarsu wrote:
| Seems to be about as accurate as a good geoguessr player on a
| time limit. Recognizable vistas are generally right down to the
| city, and even if there's only general architecture to go off
| it's often right to within a couple hundred kilometers.
|
| The explanations are a bit hit and miss. Some are great and
| correctly describe the names of buildings in the picture, some
| are only vaguely related to the picture.
|
| Ethically this is very questionable. Of course with enough
| dedication humans can do the same (e.g. Rainbolt has made a
| Youtube career out of this), but commoditizing this for every
| stalker around the world has some troubling implications.
| surfingdino wrote:
| Ethics is absent in the minds of the people building and
| financing this. AI is about wholesale value extraction and
| destruction of competition done by the ecosystem of small
| startups repackaging AI APIs. Those APIs will be turned off
| once ad revenue starts flowing into the bank accounts of AI API
| providers.
| kome wrote:
| Interesting concept, and it works somehow. But they definitely
| needs better web developers. Very strange flickering, what the
| hell is that?
| erksa wrote:
| This is close to an actual need I've managed to create for
| myself.
|
| I do photography and I store those I want to share on nextcloud.
| In my selection and export process all metadata etc is stripped.
| But I realized too late that it also stripped out the geo-
| coordinates. No problem adding that in, but still have a laaarge
| amount of photos without geolocation data.
|
| I'm too lazy to re-export all the older ones, so being able to
| run something like this on them would be perfect. I would be
| satisfied with a general area, roughly hitting the province/state
| its taken in. It doesn't have to be accurate at all, it's more
| for my own geo grouping.
|
| This site though goes bananas on firefox/mac. Flickering and font
| adjustments..
| CaptainOfCoit wrote:
| > I'm too lazy to re-export all the older ones, so being able
| to run something like this on them would be perfect. I would be
| satisfied with a general area, roughly hitting the
| province/state its taken in. It doesn't have to be accurate at
| all, it's more for my own geo grouping.
|
| I don't think this is even close to being accurate to be used
| in this way, out of ~10 images I uploaded it got one "correct"
| (right country, wrong city). Unless you want all your images to
| geo-tagged "Somewhere, US", probably better to re-export/re-
| import with your original metadata.
| erksa wrote:
| That's fair. I couldn't even get this to work, so not in
| particular looking at this implementation. I just literally
| was thinking of if this would be viable or not as an
| approach, so it was fun to see something that tries to match
| the bill!
| solardev wrote:
| If you still have the original photos, maybe you can write a
| script to run both the originals and exports against a
| perceptual hash (so as to easily identify the correct original)
| and then just update the JPEG EXIF data of the exports?
|
| https://github.com/JohannesBuchner/imagehash
|
| Depending on specific formats, you should be able to read and
| edit metadata without having to reprocess the images. If the
| exports are named similarly to the originals, you don't even
| need to hash them.
| erkkonet wrote:
| Funny enough it was accurate all while citing items that were not
| in the picture (not even cropped out), like tall buildings and
| signs in a specific language. I'm sure there will be refined
| versions that are scarily accurate. Another OSINT tool for better
| or worse.
| karma_pharmer wrote:
| FirebaseError: Installations: Create Installation request failed
| with error "400 INVALID_ARGUMENT: API key expired. Please renew
| the API key." (installations/request-failed).
| jimlawruk wrote:
| I put in a photo of a small lake near Truckee, CA, several miles
| from Lake Tahoe, and it reported it was Lake Tahoe. It was wrong,
| but impressed it was geographically very close.
| iLoveOncall wrote:
| Now try with a photo of a lake that's on the other side of the
| world compared to Lake Tahoe and see if it doesn't also report
| it as Lake Tahoe.
| 1970-01-01 wrote:
| It's heavily biased and therefore easily tricked. I uploaded a
| photo from NYC. The graffiti on the wall is a
| clue that the photo was taken in Detroit. The vegetation in the
| background is also consistent with the climate of Detroit.
| surfingdino wrote:
| It is looking for distinct features in the photo and does a
| probabilistic match against a tagged dataset. The features that
| match best on the tagged photos in its dataset are used to
| construct output that looks like a plausible answer. Don't use
| it to plan your trip.
| usaar333 wrote:
| Yeah, gave it a photo of a beach on Lake Ontario with two Asian
| friends of mine in it. Guessed.. Japan
| KeplerBoy wrote:
| It's also a ridiculously hard task.
| ToucanLoucan wrote:
| Yeah maybe just don't do it then? If someone removes the EXIF
| data from a photo there's probably a reason for that, and
| assuming that's suspicious in some way is pretty ridiculous
| in a society that's supposedly all about personal freedom and
| the right to a fair trial.
|
| I don't mean to be aggressive here but this seems like yet
| another tool that will be abused to shit by already powerful
| people to do even sketchier things.
| KeplerBoy wrote:
| I agree, this project probably shouldn't exist. But oh,
| well here we are and stuff like this can be built with
| reasonable effort. Scrape google streetview and every exif
| tagged Image you can get your hands on and get training.
|
| I have no idea where this is heading, but we aren't turning
| back.
| boxed wrote:
| > Sweden
|
| Good
|
| > Rural area
|
| good
|
| > [pin in the center of Stockholm, the most urban area in Sweden]
|
| ouch.. not so good.
| poulpy123 wrote:
| to be fair they say they provide the coordinates for the city
| or the town, so if it's not too far from stockholm I would
| count it right
| dmd wrote:
| I'm blown away; it correctly identified a photo taken _inside my
| house_ - just a picture of my kitchen - as being in eastern
| Massachusetts, just based on the architecture.
| CaptainOfCoit wrote:
| Maybe it does a lot better with photos related to the US,
| training set probably contained mostly US-related images, as
| only one image out of ~10 taken in various European places were
| correctly guessed for me. Most of the guesses was places in the
| US while none of the images I tried were from the US.
| fer wrote:
| There's a certain training set bias. Most pictures from post-
| Soviet states land in Moscow for me.
| Zambyte wrote:
| I wonder if it looks at EXIF data at all.
| dmd wrote:
| I stripped date and geo info.
| btasker wrote:
| I didn't have the same luck.
|
| I gave it a photo from inside a house, you can see a person on
| the bed, and the white wall behind - that's it.
|
| Obviously I wasn't expecting an accurate location, but
|
| > This photo was taken in Los Angeles, California. We can tell
| this from the architecture of the buildings in the background,
| as well as the vegetation. The palm trees are a dead giveaway
| that this is Los Angeles.
|
| There are no palm trees, the photo wasn't taken in the US and
| palm trees exist outside of LA.
|
| I also fed a photo of some quite distinctive castle ruins. It
| mislocated that by 100s of miles.
| foobarbecue wrote:
| I gave it a picture from a bar in Austin. It nailed it, but with
| some interesting hallucinations in the description. The photo had
| a small Texas flag, but nobody was wearing cowboy hats, and there
| was nothing with "Austin" on it in the photo. Description was:
|
| This photo was taken inside a bar. There are several clues that
| indicate this is Austin. First, there is a sign on the wall that
| says "Austin." Second, there is a Texas flag on the wall. Third,
| there are several people wearing cowboy hats, which is a common
| sight in Austin. The coordinates of this photo are
| dylan604 wrote:
| Is it possible that the sign that says Austin _is_ on the wall
| and is known to the system but not visible in the actual photo?
| fer wrote:
| Perhaps, but I've tried some rural landscapes without any
| sign and it came up with English signs as a hint for pointing
| England/Wales.
|
| Even photos with signs in Irish were pointing to England,
| it's half funny, half offensive.
| nirav72 wrote:
| I uploaded couple of pics of a beach in Turks and Caicos. It came
| back with a beach in the Bahamas. Not even close. But I suppose
| close enough geographically. Also a pic taken from a stationary
| train in Chicago, came back as NYC.
| Loranubi wrote:
| I uploaded a drone shot of New Taipei City and it gave me Taipei.
| Close enough. I don't know if it was cheating though because the
| image had exif gps coords embedded...
|
| The site worked fine on Firefox on iOS.
| exar0815 wrote:
| It told me the correct country, but the completely wrong city,
| and then began to describe a typical place in the style of the
| country - nothing of which was visible on the image.
| consumer451 wrote:
| I uploaded a generic photo I took of a field, dandelions, and
| trees. It confidently stated Switzerland, and provided specific
| GPS coords.
|
| Of course, it was entirely wrong.
|
| Some level of confidence indication would make a system like this
| much more useful.
| burkaman wrote:
| I think this is generative AI and it doesn't know how confident
| it is. So far it hasn't gotten any of my pictures right and
| it's made pretty bad guesses, a human could do better on most
| of them.
| consumer451 wrote:
| I can only imagine the quality of the systems being sold to
| governments, and people trusting them because "AI." I mean,
| "intelligence" is right in the name!
| dylan604 wrote:
| Intelligence is also in the name of the CIA, and that's
| pretty well understood as an oxymoron. Artificial is also
| in the name which seems much more apropos. It's clearly not
| real intelligence, it's purely artificial in the use of the
| word. I guess, Computer's Best Guess Simulating
| Intelligence To Low Intelligent Humans would be too on the
| spot and not as sexy of an acronym.
| consumer451 wrote:
| I actually have faith in the intelligence products
| produced by the modern CIA, much more than AI snake oil.
| dylan604 wrote:
| There's a difference in the products used to produce
| intelligence vs the analysis/centralization of it that
| the oxymoron comment goes towards.
| ubutler wrote:
| The LLM's logits should be translatable into probabilities
| although I'm not too sure how meaningful those might be as
| models can sometimes be quite confident in entirely invalid
| predictions.
| vineyardlabs wrote:
| I haven't done deep reading on LLM architectures and I
| don't really know if LLMs have logits in the traditional
| sense of a CNN or something, but I think the problem with
| this is that the LLM's logits would have absolutely no
| bearing on it's confidence of the location being correct,
| only on it's confidence that the tokens making up the
| answer it provides follow from the tokens that were encoded
| from the provided image, which isn't the same thing.
| barrkel wrote:
| I don't think that's the right way to think about LLM
| logits. Fundamentally the logits represent probability of
| similarity with the text it's been trained on, given the
| current prefix. Mixed in with any correspondence with truth
| is not only tone, phrasing, dialect, language syntax, but
| also stuff like the likelihood that specific details are
| related to general concepts. Even if we're talking about a
| person with three legs, or a horse riding a man, it'll be
| hard for the LLM to not assign a fairly high probability to
| sentences that describe two legs, or a man riding the horse
| and not the other way around.
| sebys7 wrote:
| 1/3 was completely wrong, in the sense that the coordinates,
| country and city had nothing to do with it but THEN the sources
| were other buildings from the actual country and city. 2/3 got
| city and coordinates correct, but got the country wrong, which
| idk how that happened. 3/3 got country, city and coordinates
| correct Pretty cool
| karaterobot wrote:
| I had ChatGPT generate some selfies taken in various places, then
| ran it through this app. My assumption was that this app would do
| really well, since one model would identify the stereotypical
| features generated by the other model. It got 1/3. It nailed
| Minneapolis, it got Damascus, Syria wrong (said Amman, Jordan),
| and it got the Ballard neighborhood of Seattle wrong (said San
| Francisco).
| plorg wrote:
| I uploaded pictures of a couple of street corners and it
| confidently identified them as being in Texas and Florida, based
| on text that was not in the pictures and, in the second case,
| foliage in a scene that included only concrete. Although in
| fairness to the model, a parking lot may be the dominant
| ecosystem in Jacksonville.
|
| Anyways, these pictures were from Iowa.
| DougBTX wrote:
| Same, location identified by the architecture of the buildings
| in the background and the car's numberplate... of a car with no
| numberplate driving through a wood.
| abnry wrote:
| I took images directly from Google Images search and it got them
| wrong. But it was sort of directionally right. My local city hall
| it said was the courthouse in the same county. The local bridge
| was put into a wrong state.
|
| Interestingly, it provided reference images and the images I
| posted were basically in the reference images.
| ethanholt1 wrote:
| It was correctly able to identify several photos of my vacation
| to NC, down to the exact location where the photo was taken on
| the hiking trail. Pretty scary. Additionally, just to be sure, I
| used an EXIF data wiper to make sure it wasn't pulling data from
| there and tried each photo in a seperate Incognito instance.
| Still got it correct, all 3 times. Mind boggling.
| teakie wrote:
| how?
| poulpy123 wrote:
| It recognized 2/7 of the pictures I used. The two success are a
| really well known place in Rome (the roman forum, although it got
| the arch wrong) and a small but very touristic city. It guessed
| the country right but was far from the place in 4 cases:
| landmarks were visible but they are not hugely touristic. In the
| last case the country was wrong, but it was a picture from my
| office with no landmark.
| Sporktacular wrote:
| It's funny that with a direct match to a precisely located photo
| in its database, it got the country right by comparing the
| architectural style, but still got the city wrong.
| rnewme wrote:
| Even though it missed the town by few kilometers it also
| recognized my wife's dress and linked to the webstore for it
| hhh wrote:
| FYI this site is keeping everything you upload in a Google
| storage bucket, which was unauthenticated up until a little bit
| ago. (Full disclosure, it's my tweet.)
|
| https://twitter.com/spuhghetti/status/1786033761341083731
| Zeratoss wrote:
| thotDBSmash ??
|
| Does this imply that the people behind the website specifically
| saved "juicy" user-uploaded images?
| hhh wrote:
| no, it looks like it was a separate project, but stored in
| the same bucket. In the time I had access to the bucket (it's
| no longer public), it looks like they were scraping images
| from a dating site/app and each directory represented a
| profile.
| ShamelessC wrote:
| That doesn't sound super sketchy or anything.
| pphysch wrote:
| Why use a LLM for this? You'd definitely want a large model, but
| this seems like a more straightforward classification problem
| that doesn't require understanding of language.
| andoando wrote:
| I uploaded a photo of a screenshot of a chess game on chess.com
|
| It identified as the golden state bridge of San Francisco, saying
| the buildings in the background are also consistent with the
| architecture in San fran.
| onemoresoop wrote:
| Entirely wrong result.
| salade_pissoir wrote:
| This seems like another Hotdog/Not Hotdog business model.
| underlogic wrote:
| I think it just uses EXIF data, then makes guess of other photos
| from same IP. Fake it till you make it
| vel0city wrote:
| Uploaded a photo of the Bell Centre. Easy Habs logo on it. City
| location: Toronto.
| ghastmaster wrote:
| > The photo was taken from a tall structure, possibly a fire
| tower.
|
| It was way off on the state, but I am still impressed with that
| spot on description. It was taken from a fire tower.
| alistairSH wrote:
| Tried it a few times, it's hit or miss.
|
| - Rooftop bar in Viejo San Juan, PR was identified correctly,
| down the intersection.
|
| - Beach on the south coast of Vieques, PR was identified as
| Jamaica, so reasonably close for a non-descript tropical beach.
|
| - Office building in Reston, VA which is fairly obvious
| (biggest/tallest building in the area) was identified as being in
| San Jose, CA.
|
| - Train station in Staunton, VA was identified as somewhere in
| Massachusetts.
|
| The attributes of the photo were mostly accurate, but were
| matched to an incorrect location.
| itslennysfault wrote:
| I've been learning deep learning, and I built a very toy version
| of this recently. It's really just a classifier that can (maybe)
| tell you if a photo was taken in one of the 5 cities I trained it
| on.
|
| https://huggingface.co/spaces/itslenny/fastai-lesson-2-big-m...
| stainablesteel wrote:
| it seems to be a rule of thumb that you can pick a subreddit that
| operated as some kind of service, ie r/whereisthis, and replace
| that entire apparatus with an ai of some kind
| glonq wrote:
| I gave it a snippet of Montevideo city skyline, and it responded
| with:
|
| The photo was taken from a rooftop in Buenos Aires, Argentina.
| The photo shows a clear view of the city's skyline, including the
| iconic Obelisk of Buenos Aires. The buildings in the photo are
| characteristic of Buenos Aires' architecture and the vegetation
| is typical of the region.
| K0balt wrote:
| I uploaded a nondescript scenery photo with no non-natural cues
| from the Dominican Republic, it got the general area right within
| 50km.
| timnetworks wrote:
| Googled 'vacation photo' and picked some off of Flickr. The
| locations matched the captions, State and Country (FLA and
| Cancun) correctly.
|
| Obviously uploading a picture of a hot dog will waste compute on
| trying to figure out what kind of traffic the ketchup is, but it
| works with snapshots great (not stock photos)
| fragmede wrote:
| The question is, how does this do at GeoGuessr, where users are
| given a picture from Google street view, and are asked where it
| is on the world by clicking on a map of the world. Users get
| points based on how close it is, user with the most points after
| N rounds, wins.
|
| The best player in the world, Rainbolt, played against an AI out
| of Stanford, so I wonder how this one would do.
|
| https://www.geoguessr.com/
|
| https://www.youtube.com/watch?v=ts5lPDV--cU
| Alifatisk wrote:
| Reminds me of a research paper that used ai to accurately pin
| point where picture was taken, I had hoped this was it. But it's
| better than nothing.
| noashavit wrote:
| scary accurate
| mightytravels wrote:
| Claude Vision can do that for you if you are building something
| similar. Had similar results with OpenAI.
|
| https://docs.anthropic.com/claude/docs/vision
| salamo wrote:
| Neat demo. It seems like there may be a few things happening in
| tandem.
|
| I uploaded a picture of a forest and it came back with visually
| similar images. So the first thing it might be doing is some kind
| of KNN, and if the pictures have location labels associated
| applying some sort of weighted average to determine GPS
| coordinates. This is pretty cool.
|
| I also tried flipping the image horizontally, and it came back
| with the same images. So their embedding isn't based off of exact
| matches (good) and seems to be invariant to some basic
| translations (good). It also seems like it's directly extracting
| visual features from the image. This can be done with something
| like Blip[0].
|
| Then I uploaded a screenshot from Magic School Bus. It still
| extracted information to guess the "location" of the cartoon (San
| Francisco, which is wrong). So that's probably how it works.
|
| I also found the text output is similar in some ways with
| OpenGVLab InternViT [1]. So perhaps this or something like it is
| being used to extract features.
|
| And of course there may be an LLM on top of these extracted
| features with some sort of prompt template. But I should add that
| the text explanation is the _least useful part of the result_ ,
| since it is unreliable and less informative than the "boring"
| similarity metrics above.
|
| [0] https://huggingface.co/Salesforce/blip-image-captioning-
| larg... [1] https://internvl.opengvlab.com/
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(page generated 2024-05-02 23:02 UTC)