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 ;-)
yassa9 20 hours ago [-]
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 !
jambalaya8 19 hours ago [-]
agree! AWESOME work!
bmurray7jhu 21 hours ago [-]
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.
4gotunameagain 17 hours ago [-]
Rumour has it that they achieved the first TERCOM using the then revolutionary bit slicing technology.
yassa9 21 hours ago [-]
oh, wow,
I didnt know that existed, thank u, sure gonna look into it
zer0x4d 17 hours ago [-]
Super fun! Interestingly, this is how JPL was able to significantly reduce the Mars 2020 landing radius on Mars. Cameras onboard take pictures of the terrain and match that to maps to figure out where the lander is. https://www-robotics.jpl.nasa.gov/what-we-do/flight-projects...
yassa9 17 hours ago [-]
omg wow, thats super hard, although cool ,
zer0x4d 16 hours ago [-]
It was cool, very fun 3 years of my life working as a part of that team :)
CamperBob2 11 hours ago [-]
Thanks for your service! Awesome work, I'm envious.
lexlambda 21 hours ago [-]
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.
GaryNumanVevo 20 hours ago [-]
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"
yassa9 21 hours ago [-]
yea , heard about them before, but didnt know that whole treasure till I really used it , impressive
dwa3592 19 hours ago [-]
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
deiptx 17 hours ago [-]
I find it highly ironic that his is the second article on the main page right after "avoid building technologies that could be used by a police state".
E-Reverance 8 hours ago [-]
I don't see it, did he delete it?
fhn 16 hours ago [-]
EVERY technology could be used by a police state
esafak 14 hours ago [-]
It is a question of how adversely empowering the technology is.
treyd 11 hours ago [-]
A help with this is the sun is to the left and it seems to be midday, so you could answer the "cardinal direction" question just from the picture with "west ish", which is what it turns out to be.
yassa9 11 hours ago [-]
I tried to use the sun info , but honestly I couldn't at all,
thx for the tip
treyd 6 hours ago [-]
It'd probably be hard to do directly/algorithmically, but the shadows from the trees is what I was looking for visually.
sllabres 16 hours ago [-]
People liking this post will probably like this [1] and especially these [2] from the channel. All solved using algorithms and map data.
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
phalanxx 20 hours ago [-]
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.
yassa9 20 hours ago [-]
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
StilesCrisis 20 hours ago [-]
Just by reading your actual messages it's easy to see that you didn't write the blog post entirely by hand.
yassa9 19 hours ago [-]
ok
StilesCrisis 20 hours ago [-]
"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???)
yassa9 20 hours ago [-]
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,
voidUpdate 20 hours ago [-]
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
yassa9 20 hours ago [-]
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)
StilesCrisis 19 hours ago [-]
The camera make and model wouldn't tell you the lens parameters. The EXIF would, but that was already covered in the triplet.
voidUpdate 3 hours ago [-]
It does if you google the make and model to find out the lens parameters (assuming it isn't a fancy camera with interchangeable lenses)
bitcurious 21 hours ago [-]
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.
yassa9 21 hours ago [-]
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
mirzap 13 hours ago [-]
Awesome write up! This is now one of my favorite articles on HN.
yassa9 13 hours ago [-]
thaaank u man, I really appreciate ur comment
cecinuga 21 hours ago [-]
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
yassa9 21 hours ago [-]
thaaank you !!
Its my first ever challenge to do, and yea, I really found my passion
num42 19 hours ago [-]
Good article! Off-topic, Is Palantir doing the same thing with its internal software to geolocate?
consumer451 12 hours ago [-]
I have no idea about that particular company, but wouldn't satellite-based synthetic aperture radar datasets make this "super easy?" I would imagine so.
Assuming they (and militaries broadly) do this +more, like actually using vision models trained on billions of geolocated landscape photos.
yassa9 19 hours ago [-]
thanks ! no idea about Palantir, but in my opinion, this can not be automated , needs much manual work and tons of trial and error
Gooblebrai 13 hours ago [-]
This is beyond impressive. Very good work!
yassa9 13 hours ago [-]
glad u liked it :D
mattpk 13 hours ago [-]
> NOTE: this is a genuine human work, didnt use LLM generation.
I'm sorry, but I don't believe this. The article reads like LLM text post-edited by an AI prompted to "write like a non-native English speaker, replace you for u, make errors, etc".
The other pages on your site are cough, "the smoking gun". For instance, your "Suckless, single binary, zero-dependency CUDA/C++ inference engine for NVIDIA's DVLT. Reconstructs 3D scenes from a handful of images (depth + rays + camera pose => point cloud), no python, no torch, no framework." project.
john_strinlai 12 hours ago [-]
errors = llm, no errors = llm, any word from a list of hundreds = llm, absence of any llm-words = suspiciously like an llm instructed not to use those words, declare no llm was used = llm.
there is no winning. if you post something in 2026 or beyond, someone is going to exclaim "llm!". i feel badly for aspiring bloggers or writers. it's also getting rather annoying that 50% of comments on hn, regardless of the topic they are posted on, are the exact same comment about llms.
yassa9 11 hours ago [-]
really thank u John, I felt disappointed after those comments, someone below said that the pangram is against my text, I doubted myself and even went to online pangram : https://pangramaidetector.org/
spent literally half an hour copying each single section and paragraph (removed the code and Katex) and literally all the results are "0% AI-generated text" or max 15%
yassa9 12 hours ago [-]
haha : "write like a non-native English speaker"
man, Im actually non native speaker xDD
"replace you for u" ???? what ?!
phkahler 14 hours ago [-]
@yassa How long did this take?
yassa9 14 hours ago [-]
do u mean the whole work ?
I spent at first 3 "whole" days in research, trial and error
trying different methods and scripts, like for example tried the depth estimation to build upon it, failed many times till I gave up
then came back after a week and spent another 4 days till succeeded
then the refining, cleaning and organizing of all of that, also structuring and writing the blog, took about another 3 days
you can say that total is ~10 days of work
souenzzo 10 hours ago [-]
That's kind of seed finder but in real life
ohyoutravel 21 hours ago [-]
> NOTE: this is a genuine human work, didnt use LLM generation.
A million upvotes from me.
yassa9 21 hours ago [-]
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
ohyoutravel 12 hours ago [-]
Great content too generally. Without the disclaimer I find myself less engaged with the content knowing it could be an LLM hallucination and am ready to eject at any moment.
btw Micronesia is _not_ a country!
OkayPhysicist 12 hours ago [-]
Micronesia is, too, a country. Referring to the Federated States of Micronesia as "Micronesia" is just as legitimate as referring to the USA as "America".
ape4 20 hours ago [-]
What about tides? Would the outline of the island be different based on the time of day.
yassa9 20 hours ago [-]
honestly, I didn't think about it, I just trusted the OSM polygons
hhh 21 hours ago [-]
great blog and great writeup
yassa9 21 hours ago [-]
thannks, really grateful :D
aquafox 18 hours ago [-]
Nice, but Rainbolt would do it in under a minute ;)
yassa9 18 hours ago [-]
haha, I actually agree
naniel 18 hours ago [-]
this is really cool. fun little problem turned into great write-up, and i love that you included the code snippets. thanks for sharing
yassa9 18 hours ago [-]
really glad that you liked it
esafak 14 hours ago [-]
Good job, Yassa. This is how you get a job in the AI age.
yassa9 14 hours ago [-]
haha, I wish , this is my first OSINT challenge to solve tho
piterrro 21 hours ago [-]
really impressive, could that be the way to locate yourself without GPS? assuming we know more/less where we are
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
hnlb53nrpg 16 hours ago [-]
Not glamorous but it works
jf93ap29sh 18 hours ago [-]
Loved it.
grodes 21 hours ago [-]
impressive
NeoByte 2 hours ago [-]
[flagged]
_alphageek 11 hours ago [-]
[dead]
fenestella 21 hours ago [-]
[dead]
ligarota 18 hours ago [-]
All of this to not use Google images
melozo 18 hours ago [-]
All of this to try and learn something new
hno8a34nwn 20 hours ago [-]
This is the real takeaway
Rendered at 10:04:08 GMT+0000 (Coordinated Universal Time) with Vercel.
https://en.wikipedia.org/wiki/TERCOM
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.
thx for the tip
[1] https://www.youtube.com/@colsto
[2] https://www.youtube.com/watch?v=eY-W9gmwxhg https://www.youtube.com/watch?v=nzytWZPyuEw https://www.youtube.com/watch?v=rkmXs_7hELg
Incidentally, the image seems to be the one the resort uses on their website! https://oanresort.wixsite.com/chuuk
but you are right, I should add that
Yeah, a human definitely wrote this. Nothing fishy here. (Why would the camera make or model matter???)
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,
I observed that at the end, didnt push on it further though. It already passed and I was super exhausted
https://en.wikipedia.org/wiki/Synthetic-aperture_radar
https://eos.com/blog/what-is-sar-synthetic-aperture-radar-im...
I'm sorry, but I don't believe this. The article reads like LLM text post-edited by an AI prompted to "write like a non-native English speaker, replace you for u, make errors, etc".
The other pages on your site are cough, "the smoking gun". For instance, your "Suckless, single binary, zero-dependency CUDA/C++ inference engine for NVIDIA's DVLT. Reconstructs 3D scenes from a handful of images (depth + rays + camera pose => point cloud), no python, no torch, no framework." project.
there is no winning. if you post something in 2026 or beyond, someone is going to exclaim "llm!". i feel badly for aspiring bloggers or writers. it's also getting rather annoying that 50% of comments on hn, regardless of the topic they are posted on, are the exact same comment about llms.
spent literally half an hour copying each single section and paragraph (removed the code and Katex) and literally all the results are "0% AI-generated text" or max 15%
man, Im actually non native speaker xDD
"replace you for u" ???? what ?!
you can say that total is ~10 days of work
A million upvotes from me.
btw Micronesia is _not_ a country!
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