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.
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
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"
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
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
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,
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)
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
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
https://en.wikipedia.org/wiki/TERCOM
Incidentally, the image seems to be the one the resort uses on their website! https://oanresort.wixsite.com/chuuk
I observed that at the end, didnt push on it further though. It already passed and I was super exhausted
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,
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
A million upvotes from me.