Validating against a complete register: Germany and MaStR

Every accuracy figure this project quotes for Pakistan is bounded by the same thing: the calibration quadrats behind it (see Calibration quadrats for the current count) were hand-picked rather than randomly sampled, their completeness is attested by one mapper rather than independently verified, and that completeness is relative to the date of the background imagery used for mapping rather than the date of the Sentinel-2 composite the model actually reads. Those are real limits and no amount of additional modelling removes them.

Germany is the one place where a different standard is available. Registration in the Marktstammdatenregister (MaStR) is legally mandatory for grid-connected PV, so per-municipality rooftop capacity there is not a sample to be compared against, it is the answer. earthpv validate-mastr uses it to test the parts of the capacity chain that nothing else in this project can check.

earthpv mastr                       # once: download the register (multi-GB, hours)
earthpv validate-mastr --aoi germany \
  --solar-path /path/to/rooftopsenti/data/germany_500/osm/solar.parquet

Writes results/germany_mastr_validation.json. Everything except the last section runs in the default (no-torch) environment, needs no GPU, and takes under a minute once the register is downloaded.

What is below the detection floor, measured rather than proxied

The reason this project has two detectors instead of one is the claim that the ≥ 400 m² segmentation model is blind to most rooftop capacity. Until now that claim was supported by MaStR's "72.6% of rooftop capacity is in units ≤ 100 kWp" — a round-number proxy, quoted nationally.

The register carries exact per-unit capacity, so the share below the project's own floor can be measured directly. 400 m² of module area at 0.18 kWp/m² is 72 kWp:

Unit size	Share of rooftop capacity	Share of rooftop installations
≤ 10 kWp	24.0%	60.0%
≤ 30 kWp	56.8%	93.9%
≤ 72 kWp (the 400 m² floor)	65.5%	97.2%
≤ 100 kWp	72.6%	98.5%
≤ 300 kWp	83.2%	99.6%
≤ 1000 kWp	96.4%	100.0%

Measured 2026-08-11 on 4,411,015 rooftop units totalling 74.8 GWp, commissioning cutoff 2025-09-30. The ≤ 100 kWp row reproduces the figure this project already quotes, which is the check that the filters here match mastr.aggregate_gemeinden's.

The vertical gap between the two curves at the floor is the whole caution about quoting this result: the count share saturates almost immediately because small units dominate any register by number, while capacity accumulates slowly because one industrial roof outweighs hundreds of homes.

Two things to take from this table. First, the claim holds and is now measured at the right threshold: 65.5% of German rooftop capacity sits below the segmentation floor, so an instrument that only sees above it is describing roughly a third of the quantity a "rooftop solar" headline implies. Second, the capacity and installation columns are very far apart — 97.2% of installations against 65.5% of capacity — and quoting the count share when the reader will hear the capacity share overstates the gap substantially. Both belong in any statement of this result.

The same share is not transferable as one number

This project transfers Germany's share to Pakistan as a constant, to sanity-check its own sub-400 m² total (docs/methods/density.md's MaStR-shape transfer, which implied roughly 5.9 GWp). A transfer like that is only as good as the constancy of the thing being transferred, and measured across 10,675 German municipalities with at least 100 kW of rooftop capacity, it is not constant:

	Share below 72 kWp	Share below 100 kWp
National (capacity-weighted)	0.655	0.726
Unweighted mean over municipalities	0.724	0.785
Median municipality	0.762	0.845
5th – 95th percentile	0.249 – 1.000	0.295 – 1.000
Standard deviation	0.235	0.228
Spearman vs. municipality capacity	-0.42	-0.47

The negative rank correlation is the part that matters, because it is a bias with a known direction rather than just noise. Rooftop capacity concentrates in municipalities with large industrial roofs, and those have a lower small-PV share than a typical municipality. So the unweighted average across municipalities (0.724) sits 7 points above the national capacity-weighted figure (0.655), and any transfer that reasons from "a typical place" rather than from a capacity-weighted total will overstate the small-PV share. The 5^th-to-95^th spread of 0.25 to 1.00 sets the wider caution: this is a quantity that varies by a factor of about 2.7 between the 10^th and 90^th percentile municipality within a single country, so carrying it across a border as a point value is a much weaker step than the tidy national percentage makes it look.

OpenStreetMap cannot serve as the complete reference, even in Germany

A natural plan is to skip the register and use German OSM as ground truth, since Germany is famously well mapped. Measured against MaStR, that plan does not survive.

The measurement has to avoid one specific circularity. data/calibration/completeness.parquet defines completeness as 0.18 x osm_area / kw_rooftop, which already assumes the very kWp/m² constant one might want to test; selecting "well-mapped" municipalities with it and then measuring kw_rooftop / osm_area is selecting on the estimator. So osm_completeness_by_count measures completeness by unit count against the register instead, which involves no area and no conversion constant at all.

National result: of 4,411,015 registered rooftop units, OSM has 3.6%. The well-mapped tail is thin, and the implied constant is unstable across it:

Minimum count completeness	Municipalities	Pooled implied kWp/m²
≥ 30%	55	0.239
≥ 50%	18	0.083
≥ 80%	3	0.069

Per-municipality implied values span 0.02 to 0.99 against the project's 0.18. A spread like that is not sampling noise around a true value: nothing in OSM records whether a generator:source=solar polygon outlines the panel array or the whole roof it sits on, so the ratio is measuring mapper convention. The module constant therefore stays as calibrated, and this route is recorded as a measured negative result rather than a blocked one.

Two consequences travel beyond Germany. "Germany is well mapped in OSM" is true of buildings and false of rooftop PV, and the two are easy to conflate. And the same array-versus-roof ambiguity applies to this project's Pakistani OSM reference, which is used both as a recall denominator and as the evidence atlas's own hand-mapped population — it is not exempt.

The end-to-end comparison, and what still blocks it

validate_density_against_mastr zonal-joins a density run's grid onto German municipalities and reports, per estimator, the origin-forced OLS slope (multiplicative bias, 1.0 = right on average), the median per-municipality ratio, Spearman rank correlation and log-log Pearson. Because MaStR is complete, a slope against it is a real accuracy statement rather than a comparison of two estimates — which is exactly what Pakistan cannot provide.

It reports its own imagery coverage and refuses to describe a partial-coverage result as national. That guard is not hypothetical: a run covering 4 of Germany's 76 MGRS tiles would produce a national sum around 5% of the truth, and the resulting slope would read as a catastrophic model failure rather than as missing imagery.

As of 2026-08-11 this section cannot run, and the blockers are data acquisition rather than missing code:

1. Imagery. data/composites/germany/ does not exist. The sibling rooftopsenti project has composites for 14 of the 76 MGRS tiles Germany's bbox needs, and the existing data/predictions/germany/prob/ holds 4 tiles, written per-MGRS-tile rather than in the per-0.1°-cell layout density reads. Closing this means earthpv compose --aoi germany followed by infer, both resumable and network-bound.
2. A building layer with small roofs. roofclf scores per building and needs the sub-400 m² population. Germany has only the Overture ≥ 500 m² set here (1.6M rows); data/vida/DEU.parquet is absent.
3. Mapped quadrats. Every calibration quadrat is Pakistani, and roofclf cannot be fit without exhaustively mapped ground where a no-PV building is a real negative. The 3.6% OSM completeness measured above means German quadrats have to be mapped, not derived from an OSM pull — the same cost as the Pakistani ones.

Until then, validate-mastr reports density_vs_mastr: {status: absent} and the three register-internal sections above stand on their own, since none of them need imagery.

Because the harness cannot be exercised against real data yet, it is covered by tests/test_mastr_validation.py, which feeds the register back through it synthetically: a grid carrying exactly MaStR's own capacity must return slope 1.0, one carrying half of it must return 0.5, and a 200-municipality grid must be refused as non-national. That pins the arithmetic — units, the origin-forced fit, the kW-to-MWp conversion, the coverage guard — rather than merely running the code.