Excellent write up and an enjoyable read! Reminds me of the “good old times” where posts on HN were written by humans and with a specific writing style like yours.
You could’ve used a little bit more of geoguessing to narrow down results, or do a brute force visual check on the last hundred or so ;-)
yea, thanks :D , I used a tiny idea from geoguessing, that I banded the search on islands only in latitude between -30 to +30 deg. based on the sky and the tropical vibes in the img , and it worked !
For drones and missiles, this technique is known as Terrain Contour Matching. If terrain contour are measured optically, navigation is independent of RF jamming, unlike GNSS.
It’s an effective a surprisingly old technique, being used on cruise missiles as early as the 1960s. It actually precedes GPS and satellite navigation by several decades. Im continuously blown away by what engineers were able to do in that era with such limited computing power. Take a look at SAGE, for example.
Fun fact: the usage of TERCOM in the tomahawk missile actually limited its ability to be used in Operation Desert Storm. Routes had to be planned to go around actual topographical features, instead of hundreds of miles of flat desert.
OpenStreetMap data really is a godsend for such OSINT purposes.
Works much better in populated areas too, with more features like roads, shops, electric lines that can be used to search.
Claude / Gemini + OSM Turbo is a crazy you can do natural language queries like "find me a bus stop in germany that's surrounded by more than 5 three story buildings"
This is awesome. I worked on something similar a few months ago. It is a general purpose navigation system based on TERCOM and dead reckoning - https://github.com/deepanwadhwa/anumaan
thanks, and yea, it should be solved easily by passing the img to google lens, the website is the first result, but I found a fun opportunity to solve it in different way
yea, good observation, my guess is its the data more than the filter. OSM coastline polygons are generalized to different degrees depending on who traced them and from what imagery, so the fine shape detail a halo check would key on often is not in the geometry at all.
I observed that at the end, didnt push on it further though. It already passed and I was super exhausted
they literally memorize and get patterns of every possible road, place, map of any area (scanned by google earth), getting exact coordinates from single image, and play competitions and world cup based on that
they do really nice videos about finding places in old photos people ask for
I meant the blog itself, the writeup, the steps and the walkthrough all by hand
, the final code u see is llm refined, of course, I wont publish my messy and spaghetti files with much tests, failures and dead ends, also vizualizations functions to produce that green maps , and faulty versions of them
yea thank u, that's another part, but mainly it would hard although knowing that, because you need to know elevation of the drone or the camera, which is also extremely difficult (I already mentioned that in the blog)
ok, if u came with the whole conclusion by only this line, ok
, but to answer u, ( I hate to justify myself , but have to )
I started writing the blog after I started solving another challenge from gralhix : https://gralhix.com/list-of-osint-exercises/osint-exercise-0...
and the part of the solution came from the metadata, the camera model, you can check urself, so when I came back to write the blog, it just came by flow,
haha, thanks :D
I was hesitant to whether write it or not,
but I really really despise llm generated posts and blogs
and im glad someone appreciated it
Excellent write up and an enjoyable read! Reminds me of the “good old times” where posts on HN were written by humans and with a specific writing style like yours. You could’ve used a little bit more of geoguessing to narrow down results, or do a brute force visual check on the last hundred or so ;-)
yea, thanks :D , I used a tiny idea from geoguessing, that I banded the search on islands only in latitude between -30 to +30 deg. based on the sky and the tropical vibes in the img , and it worked !
agree! AWESOME work!
For drones and missiles, this technique is known as Terrain Contour Matching. If terrain contour are measured optically, navigation is independent of RF jamming, unlike GNSS.
https://en.wikipedia.org/wiki/TERCOM
It’s an effective a surprisingly old technique, being used on cruise missiles as early as the 1960s. It actually precedes GPS and satellite navigation by several decades. Im continuously blown away by what engineers were able to do in that era with such limited computing power. Take a look at SAGE, for example.
Fun fact: the usage of TERCOM in the tomahawk missile actually limited its ability to be used in Operation Desert Storm. Routes had to be planned to go around actual topographical features, instead of hundreds of miles of flat desert.
oh, wow, I didnt know that existed, thank u, sure gonna look into it
Nice, but Rainbolt would do it in under a minute ;)
haha, I actually agree
Really great article! OP, you did an awesome job breaking down a complex problem into manageable chunks and synthesizing the solution.
thanks, appreciate it
OpenStreetMap data really is a godsend for such OSINT purposes. Works much better in populated areas too, with more features like roads, shops, electric lines that can be used to search.
Claude / Gemini + OSM Turbo is a crazy you can do natural language queries like "find me a bus stop in germany that's surrounded by more than 5 three story buildings"
yea , heard about them before, but didnt know that whole treasure till I really used it , impressive
This is awesome. I worked on something similar a few months ago. It is a general purpose navigation system based on TERCOM and dead reckoning - https://github.com/deepanwadhwa/anumaan
this is really cool. fun little problem turned into great write-up, and i love that you included the code snippets. thanks for sharing
really glad that you liked it
Excellent read, I loved it.
Incidentally, the image seems to be the one the resort uses on their website! https://oanresort.wixsite.com/chuuk
thanks, and yea, it should be solved easily by passing the img to google lens, the website is the first result, but I found a fun opportunity to solve it in different way
Good article! Off-topic, Is Palantir doing the same thing with its internal software to geolocate?
thanks ! no idea about Palantir, but in my opinion, this can not be automated , needs much manual work and tons of trial and error
I read all the process, literally awesome, i don't do OSINT (i know only what is this) and i think that's very cool
thaaank you !! Its my first ever challenge to do, and yea, I really found my passion
Loved it.
It’s interesting that most top contenders don’t pass the eyeball halo check, seems like there’s room to optimize that filter in code.
yea, good observation, my guess is its the data more than the filter. OSM coastline polygons are generalized to different degrees depending on who traced them and from what imagery, so the fine shape detail a halo check would key on often is not in the geometry at all.
I observed that at the end, didnt push on it further though. It already passed and I was super exhausted
What about tides? Would the outline of the island be different based on the time of day.
honestly, I didn't think about it, I just trusted the OSM polygons
great blog and great writeup
thannks, really grateful :D
really impressive, could that be the way to locate yourself without GPS? assuming we know more/less where we are
yea, search about geoguessing on youtube, people like Rainbolt, https://www.youtube.com/@georainbolt
they literally memorize and get patterns of every possible road, place, map of any area (scanned by google earth), getting exact coordinates from single image, and play competitions and world cup based on that
they do really nice videos about finding places in old photos people ask for
What do you mean by no LLM generation if an LLM did all the coding based on reading through the .py files? Pangram isn't kind to "your" text either.
I meant the blog itself, the writeup, the steps and the walkthrough all by hand , the final code u see is llm refined, of course, I wont publish my messy and spaghetti files with much tests, failures and dead ends, also vizualizations functions to produce that green maps , and faulty versions of them
but you are right, I should add that
Just by reading your actual messages it's easy to see that you didn't write the blog post entirely by hand.
ok
"No EXIF, no GPS, no camera make or model."
Yeah, a human definitely wrote this. Nothing fishy here. (Why would the camera make or model matter???)
If you know the camera make and model, you might be able to get lens parameters and get better estimates of real world geometry from the image
yea thank u, that's another part, but mainly it would hard although knowing that, because you need to know elevation of the drone or the camera, which is also extremely difficult (I already mentioned that in the blog)
The camera make and model wouldn't tell you the lens parameters. The EXIF would, but that was already covered in the triplet.
ok, if u came with the whole conclusion by only this line, ok , but to answer u, ( I hate to justify myself , but have to ) I started writing the blog after I started solving another challenge from gralhix : https://gralhix.com/list-of-osint-exercises/osint-exercise-0...
and the part of the solution came from the metadata, the camera model, you can check urself, so when I came back to write the blog, it just came by flow,
impressive
> NOTE: this is a genuine human work, didnt use LLM generation.
A million upvotes from me.
haha, thanks :D I was hesitant to whether write it or not, but I really really despise llm generated posts and blogs and im glad someone appreciated it
This is the real takeaway
All of this to not use Google images
All of this to try and learn something new