10h 25m 16s logged

Chronovisor — Devlog 1 🍓😺

So I had an idea that probably sounds a bit out there at first glance: What if you could point at any spot on Earth and get a read on whether something is buried underneath? And what if you could do it without ground-penetrating radar, drones, or anything physical—just satellite data and a bunch of free APIs?

I decided to see if I could actually build it. I’m calling it Chronovisor.

What I actually did

The core of the project is a satellite data engine that pulls imagery directly from Google Earth Engine. Right now, I’m tapping into Sentinel-2 data (10m resolution, available from 2017 onward) and calculating spectral indices directly from the raw band data. I’m tracking:

NDVI (Vegetation health)
NDWI (Moisture levels)
NDBI (Built-up/urban areas)
Plus: BSI, SAVI, and NDMI.

The underlying theory is straightforward: buried structures mess with the surface. A stone foundation stops roots from growing properly. Ancient walls alter water drainage. Different materials retain heat differently. If you can read those subtle patterns from orbit over time, you can potentially spot an archaeological site or a forgotten structure without ever setting foot on the ground.

For the heavy lifting, I built an anomaly detection pipeline that flags anything sitting two standard deviations away from the mean across the time series. The math isn’t overly complex, but it’s highly effective. When a specific coordinate consistently shows up as “weird” across multiple independent satellite passes, you know you’ve got a real signal.

The piece I’m most proud of is the structural probability scoring. I detrend the NDVI and thermal data to strip out seasonal spikes, then check if they’re inversely correlated. If you have uncharacteristically low vegetation right where the thermal signature is unusually high (or vice versa), that’s the classic signature of subsurface interference. The code evaluates this and outputs a score from 0 to 100. It doesn’t declare a definitive “find”—it just screams, “Hey, look closer at this exact spot.”

On the infrastructure side, I set up:

A FastAPI backend utilizing a two-tier cache (in-memory + disk) with SHA-256 hashing to keep things snappy.
A clean CLI entry point for quick scripting.
A Leaflet-based frontend styled in a dark theme (obviously).

Managing Expectations: What it actually does

I want to be completely transparent about what this tool is—and what it isn’t.

This system does not tell you what is underground. It analyzes surface patterns and scores how likely it is that something is disrupting them. When the system flags a “possible buried structure,” it is strictly saying that the vegetation and thermal patterns match the footprint of subsurface interference. Nothing more.

The signal processor uses real analytical tools—Fast Fourier Transforms (FFT), harmonic detection, and autocorrelation. However, it requires legitimate sensor data to function. It doesn’t conjure findings out of thin air, and the EM field mapping elements are tools for processing input data, not magic discovery engines.

What’s next

Right now, the satellite engine is great at spotting anomalies, but it has zero context about the ground it’s looking at.

Next up, I’m building connectors for:

Local soil composition data
Seismic activity logs
Water table estimates
An archaeological database queried via Wikidata SPARQL.

The goal is to evolve the output from a basic “this spot has weird NDVI” into something incredibly actionable: “This spot has a strange NDVI anomaly, sits on a clay soil type known to preserve structures, is located within 2km of a known ancient settlement, and suffers from low seismic noise.”

That’s the kind of signal that gets interesting. Stay tuned.