Chronovisor is a satellite-based archaeology tool. You point it at any coordinates on Earth and it pulls real imagery from Google Earth Engine, runs spectral analysis on vegetation and thermal patterns, cross-references soil composition, seismic activity, water tables, and known archaeological databases, then fuses all of that into a single score that says "this spot has archaeological potential" or "nah, probably nothing."
Today was about giving the satellite engine a skeleton key.
The satellite engine could see, but it couldn’t understand. Day 2 was all about layering context on top of context until patterns start to surface.
Pleiades, Wikidata, GBIF, and VIIRS nightlights all get matched against each other. If an ancient settlement shows up in three sources but the area is now empty, that’s a red flag.
Took every data source and wired them into a 7-component weighted scoring system:
Satellite Anomaly Score (35%)
Signal strength × data quality × inverse correlation bonus when NDVI/NDWI/thermal agree.
Soil Preservation (20%)
Clay > 350g/kg = +35%, Sand > 600g/kg = -25%, pH 6.5–8.0 sweet spot.
Seismic Filter (10%)
20+ quakes = -25%, Moderate = -10%, Quiet = neutral.
Water Table Depth (10%)
<5m = -20%, >30m = +20%, No data = +30%.
OSM Historic Context (10%)
Historic features nearby = +30%, Dense = neutral, Sparse = survey opportunity.
Temporal Consistency (10%)
Significant NDVI trend + $R^2 > 0.3$ = +30%, Change events = +5%/event.
Web Archive Evidence (5%)
Archaeological content in history = +55%, Multiple hits = +25%.
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.
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:
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:
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.
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:
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.
