Advanced Tech: Geolocation from Sound Recording

I am rarely blown away by tech demos online, but Colsto’s OSINT experiment made me want to share this. . He located a blurry security-camera video of trees to within roughly 100 m using faint aircraft noise in the background.

So, OSINT stands for Open-Source Intelligence, which is analysis of publicly available information. Here, it starts with the Doppler effect. As a propeller aircraft approaches and moves away, its tonal sound changes pitch. On a spectrogram, which plots frequency against time, this produces a curved trace. Its shape depends on aircraft speed and how close it passed the microphone.

Finding Geolocation from Sound Recording

The idea is to work backwards. Known flight tracks let you predict curves at candidate microphone locations and compare them with the audio. A 3km sound path takes around 9s at 343m/s. Ground elevation, altitude, turns and changes of speed all affect the timing and shape.

Colsto found two aircraft signatures 920s apart. Searching historical US flight data, he discarded places where speed, distance or timing did not match. Silence between events removed places where other flights should have been heard. Matching both curves identified one site, verified in Street View.

Colsto hasn’t released his original code, although independent implementations have already appeared. The approach can be reproduced, but its reliability on random recordings remains unproven.

Now imagine a city like Mumbai, where the airport in the city handles roughly 950 flights daily. More flights offer more clues, but jets, traffic and reflections from buildings complicate the signal. Technically any long enough random recording will allow you to pinpoint a location. I can see how this technology could be misused, but the engineering behind it is genuinely mind-blowing. Please search online to watch the whole video if you have the time. It’s very good.

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Tech Explained: MetaLens

I was recently playing around with ST’s VL53L8CX time-of-flight (ToF) sensor. It sends out 940nm infrared light and measures how long reflections take to return. ST uses two metalenses inside this tiny module, and that sent me down a rabbit hole in optics to figure out what that is.

Think of an ordinary camera lens. Its curved glass or plastic surfaces bend light until it reaches a focus. A metalens can shape light using a surface that is almost flat. Across it sit millions of nanometre-scale pillars, each deliberately sized or shaped.

What is MetaLens?

Light really does pass through these structures in common designs. Each pillar changes the phase of the passing light, meaning how that small part of the wave lines up with the rest. Together, they create an outgoing wave that converges at a point, or spreads light into a chosen pattern. That is how a flat surface can perform the job of a curved lens. The pattern can also spread a laser beam into a designed shape, which is useful in sensors.

What are they made from? Usually a transparent substrate, often glass, with patterned materials such as silicon for infrared, or titanium dioxide and silicon nitride for visible light. Manufacturers use chip-making methods like lithography and etching to define the pillars. Nanoimprinting, rather like stamping, can replicate a master pattern for volume production. The patterned layer can be around 1 µm thick.

Applications are limitless. You can make compact optics, shape a laser beam precisely and sometimes replace multiple optical elements. They’re already in depth sensors, facial sensing and other specialised modules. But wide-angle, full-colour cameras are harder. Different colours don’t automatically focus at the same point, efficiency can fall and large areas demand extremely precise manufacturing.

One detail I think is worth keeping in mind: an almost-flat optical layer doesn’t make an entire camera that thin. Light still needs some distance to travel from the lens to the sensor.

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