they literally memorize and get patterns of every possible road, place, map of any area (scanned by google earth), getting exact coordinated 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
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"
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
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.
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".
I’ve been to Marshall Islands, Kiribati, and FSM but never heard FSM called “Micronesia;” usually when people refer to Micronesia they’re referring the cultural region inclusive of FSM and the other two I mentioned, among others.
That’s cool, didn’t realize that slang existed, today I learned, thanks!
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.
Memory on the first cruise missiles was so sacred though that they it could only store the pre-planned flight terrain data. So they not only loaded target coordinates, but full flight plans and had to launch from the programmed position. Desert was difficult as it has too few features.
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
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 ;-)
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,
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
Weirdly enough, I'm working on a similar system for position tracking in canyons. It's hybrid, uses a combination of Kalman filters, particle filters and GPS (EDIT: and LIDAR based DEM) and is based on the obvious (in hindsight) realisation that the view of the sky to get an accurate gps fix is inversely proportional to the constraints of the terrain. If you have a low field of view of the sky, you can use sensor data to constrain your likely position. I should have hardware samples for testing in the next month or so. Hopefully my theory stands up!
Not yet. I currently have one private repo with everything in it (hardware design, firmware, experiments, notes etc.). Want to clean it up before I release anything.
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
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.
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...
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".
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[ 3.0 ms ] story [ 176 ms ] threadthey literally memorize and get patterns of every possible road, place, map of any area (scanned by google earth), getting exact coordinated 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.
btw Micronesia is _not_ a country!
That’s cool, didn’t realize that slang existed, today I learned, thanks!
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.
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,
Incidentally, the image seems to be the one the resort uses on their website! https://oanresort.wixsite.com/chuuk
https://en.wikipedia.org/wiki/Synthetic-aperture_radar
https://eos.com/blog/what-is-sar-synthetic-aperture-radar-im...
[1] https://www.youtube.com/@colsto [2a] https://www.youtube.com/watch?v=eY-W9gmwxhg [2b] https://www.youtube.com/watch?v=nzytWZPyuEw [2c] https://www.youtube.com/watch?v=rkmXs_7hELg