A machine for finding the question

Hour zero: one pixel, three formulas

Before any data was pulled, one tile over Delhi was read and three de-quantisation formulas were tested against it. The obvious one — linear — produces vectors with a norm of about 2.5. The square law produces 1.0001, which is what a unit-sphere embedding is supposed to give.

The national pull

Eleven thousand seven hundred and forty-nine tiles, nine years, fifty-one minutes. Read directly from cloud storage rather than downloaded, and sampled from the overview pyramid rather than at full resolution — nineteen terabytes of source becomes 242 megabytes by choosing the right level.

Every layer below is drawn from the same 318,706 cells. Switch between them — the country is the same, only the question changes:

ChangeBuilt-upLand typesPopulationRainfallElevation

Cosine distance between each cell’s fingerprint this year and last. Bright means the surface changed. This is the layer we built the whole instrument to produce — and the one the referee later showed measures volatility rather than development.

The faint curved seams across the north are a known-open bug in the UTM zone joins. They are disclosed in the data dictionary and left visible here rather than retouched out.

A second domain, the same night

Three hours after the first grid finished, we pointed the same method at something with nothing in common with satellites: NASA’s battery ageing corpus. Thirty-six gigabytes from public storage — not the convenient mirror, which silently drops the impedance cycles. A MATLAB struct-array became five normalised tables and 7.28 million rows, and the data dictionary was written before any analysis ran.

Four connectors, two bugs

Population, elevation, districts and built-up area were joined onto the grid. Two bugs came out of it, and both are the dangerous kind, because both produce numbers that look entirely reasonable.

A negative window offset shifted the whole population raster by 239 rows. Delhi came back at 263 people per square kilometre instead of 23,452 — wrong by two orders of magnitude, and still a number you could put in a table without anyone blinking.

Summing counts over overlapping sample boxes gave India 1.75 billion people instead of 1.34 billion. Correct arithmetic on a wrong assumption: with overlapping windows you must take the mean, not the sum.

The dashboard, and its danger

A hundred and thirty-two megabytes of binary layers, twelve views, a year slider with playback. It is the prettiest thing in the project and the least load-bearing. It looks like an answer.

The referee night

A month after the instrument was finished, we turned it on our own results. Three hypotheses, three kills, each one chained to the last.

Rainfall drives land-surface change. Dead — a calendar artifact. A national year effect swings mean drift by 2.3×, and the two dry years happen to be the two lowest-drift years.

Fine, but the swing itself is real. Dead — the ordering is inverted. Water drifts 2.11× and desert 1.88× against cropland, while built-up land drifts 0.74×. Construction makes land stiller.

Fine, but it still detects real change. Dead — matched against an independent forest-loss dataset, the detector scores an AUC of 0.486. That is worse than a coin.

Five pre-registered questions

Predictions written in code before the test ran. Two killed, two partial, one answered.

The humiliating one: a baseline of “where it grew before, it grows again” predicts construction at an AUC of 0.977. The satellite embedding manages 0.60. A single lag of the target beats the entire sixty-four-dimensional apparatus by a distance.

What survived

Rainfall volatility moves the land surface at a rank correlation of 0.357 against a null of 0.000. Rainfall level does not. Those are different claims and only one of them holds.

India’s built-up growth is a fringe phenomenon: 74.6% of 2015–20 growth landed on cells that were already between 0.5% and 5% built, up from 60.2% in the 1990s. It is the one clean positive result in the project, and it uses no embeddings at all.

Press play. Thirty-five years of building, on one fixed colour scale so the growth is real rather than a rescaling artifact:

1990

Play

Built-up fraction, 1990 to 2025. Watch the corridors thicken between the cities rather than the city centres themselves getting brighter — that is the fringe result, visible without a single statistic.