Experiments: what was tried, and what it cost

Most of what has been tried in this project did not ship. That is worth writing down for two reasons: the negative results are the map of where Sentinel-2's 10 m resolution limit actually sits, and every one of them cost real compute that nobody else needs to spend again.

This page is the register. It covers everything that was measured, whether it ended up in the pipeline or not. Every experiment has runnable code in the repository; nothing was deleted on failure. For things that are still genuinely undecided, see Open questions.

How to read the verdicts:

• shipped : part of the current main workflow
• partial : useful measured signal, but not strong enough to promote
• rejected : underperformed the alternative or failed its control
• superseded: previously shipped, but since replaced

The register

Experiment	Verdict	One line
In-domain Pakistani training chips	shipped	Tripled large-array recall in Punjab, 0.18 to 0.55. The single biggest lever found.
Building prior from VIDA Open Buildings	shipped	Makes "no building nearby" a usable false-positive signal.
Quadrats as training data, not just correction	shipped	Became roofclf, now half the main workflow.
roofclf, a per-building classifier	shipped	0.879 AUC (0.834 within size band) on 27 quadrats where segmentation scores near chance.
SPPI as a corroborating second opinion	shipped	Zero-training spectral index; agreement with roofclf sets an internal floor on the atlas's headline figure.
Coverage ratio stratified by size and density	shipped	Replaced precision as the multiplier, and a flat ratio with a measured per-stratum one.
Placement-split precision and recall	shipped	Unpooling rooftop from ground-mount moved both, in opposite directions.
End-to-end validation against a complete register	shipped	Germany ran nationally at last: 99.75% of MaStR capacity covered, and segmentation recovers about a third of what it can see.
p_unmapped from a geolocated register	shipped	Replaced Germany's p_unmapped: 0.0 floor with chance-corrected register evidence, split by placement.
Recall measured against any candidate, not the same placement	shipped	Rooftop recall was understated up to 24x because a big array overruns its footprint and the finder gets labelled ground.
The module constant, measured against a register	shipped	France put DEFAULT_KWP_PER_M2_MODULE at 0.150 kWp/m2 against the assumed 0.180. Germany could not do this at all.
Reading a register back to the imagery epoch	shipped	Dated ODRE vintages remove a 53% inflation that comes purely from PV installed after the flight.
Localizing the detector by retraining on France	shipped	Rank correlation against the register 0.29 to 0.45, closing half the gap to Germany. Slope barely moved.
The 400 m2 floor, measured from outside	shipped	Recall climbs with size (rho +0.83) while the sub-metre control stays flat (-0.29). Retraining on French data does not move it.
roofclf on French residential PV	rejected	0.627 AUC within size band against Pakistan's ~0.834. A 20 m2 array is a fifth of a Sentinel-2 pixel.
13x the roofclf labels, from OpenPVMapper	rejected	16,435 positives against 1,231 moves AUC by -0.005. The model scores 0.683 even on its own label source.
Authoritative footprints instead of VIDA	partial	VIDA misses 58% of French buildings. Fixing that is worth +0.027 AUC, which is 14% of the gap to Pakistan.
roofclf transfer to Germany	partial	A foreign model transfers (0.70-0.72) and it barely matters which country; in-domain German training is worth +0.08.
The sub-400 m2 estimator against a complete register	partial	Beaten by total roof area in Germany (23% vs 58% error), beats it 3.4x in Pakistan. Regime-specific, and a perfect national total hid 47% per-municipality error.
A register-calibrated German roofclf half	partial	52.37 GWp against a registered 54.29, but still behind a roof-area baseline (48.4% vs 37.8% error), so it is reported beside the atlas rather than inside it.
Register coordinates as roofclf labels	rejected	+0.023 building AUC, 15.5 points WORSE municipal error: the geolocated band is 6.9 of 49.0 GWp, so the estimand and the labels disagree.
Register counts as a known class prior	partial	Fixes the label problem (33.4% against 70.6%) but ties the roof-area baseline on representative municipalities. The 18.2% first result was a PV-dense sample.
Size-stratified adoption intensity	shipped	First estimator to beat the German roof-area baseline: 35.4% against 37.8%, using the 200-400 m2 band and no classifier.
Size-dependent OSM rooftop conversion	shipped	Ground reconciled at 0.91; rooftop was 2.5x over because large polygons are roof outlines (0.051 kWp/m2) not arrays (0.200). Germany Verified 38,508 -> 25,921 MWp.
RID as a control on the size relationship	partial	Exhaustive labels confirm the band but refute the mechanism, and price OSM's size bias at +0.729 against a true +0.349.
OpenPVMapper as an external reference	partial	91% of the register's sub-72 kWp capacity, but 0.67 recall against hand-mapped truth and a model output throughout.
SPPI alone as Germany's sub-400 estimator	rejected	Orders German roofs better than the trained classifier does (33.7% against 36.8% municipal error) and still loses to crediting roof area with no classifier at all (31.2%).
Dividing register p_unmapped by an OSM positive control	rejected	The control is contaminated by the same sub-30 kWp coordinate suppression it was meant to absorb.
OSM geometry dissolve and closest-match dedup	shipped	Nested plant/generator ways were double-counting real installations.
Recall correction and credible intervals	shipped	Turned a structural floor into an estimate with a stated interval.
Recall correction for roofclf, not just segmentation	shipped	The roofclf half was counting only the roofs it flagged; correcting it moved Best 16.6 to 18.3 GWp.
Quadrat-bootstrap uncertainty on the atlas	shipped	The headline figure now carries a 90% range.
Solar-glint corroboration	shipped	Calibrated likelihood ratios per size bucket, reward-only, never demoting.
Tile-major glint fetching	shipped	22x faster, numerically identical output.
Pre-boom epoch check	shipped	A persistent 2021 signal demotes a candidate.
Vegetation veto	shipped	596 of 5,132 leads vetoed, and specifically the right ones.
Hard-negative mining for segmentation	shipped	Bi-temporal confirmed negatives cut false-positive pixels 28.6%.
Fraction-regression head	partial	Beats segmentation on small arrays by 23x in the residential quadrat, never promoted into a published number.
Time-series step detection below 400 m²	partial	First non-zero recall under 500 m², but the capacity claim failed its own control.
Glint as a direct detector	partial	About 9% on model leads against about 1% on random buildings.
Per-pixel glint amplitude trim	partial	Moves pixel IoU slightly; threshold gating by glint does not.
roofclf hard-negative retrain from known false positives	rejected	n=6 is not enough signal; oversampling trades overall skill for it.
Quadrat-supervised fraction retrain	rejected	A large in-sample win the holdout does not support.
Two-season 20-band stacking	rejected	No recall gain anywhere; slightly worse on large arrays.
Boom-window (2021 vs current) stacking	rejected	Inconclusive: training collapsed on 332 chips.
Sentinel-1 corner reflection	rejected	Backscatter enhancement indistinguishable from speckle.
Snow-cover contrast for small rooftop PV	rejected	Sound physics, wrong calendar: 0.096 usable scenes per commune-winter outside the Alps.
Cell-aggregate glint density	rejected	Wrong statistic for the question.
Missed-installation glint recovery	rejected	Control false-validation rate exceeded the recovery rate.
Roof-axis orientation prior	rejected	Flat concrete roofs do not constrain a tilt frame's azimuth.
Standard-pose matched filter	rejected	The densest pose bin covers under 10% of installations.
Glint PSF matched filter	rejected	The PSF is real and measured, but every filter variant scores below the aperture statistic it would replace.
Verified-negative false-spike rate	shipped	2.0% against 8.7-20.3% on model-negative controls, which reopens the spike-rate density estimator.
Super-resolution, three variants	rejected	No gain, and hallucination risk on a detection task.
Two-endmember spectral unmixing	rejected	0.659 AUC, worse than both SPPI and roofclf, with a 92x scale spread.
Epoch jump / step change as roofclf features	rejected	Measured at exactly zero effect, and worse in the reflectance variant.
Keeping more than the median composite	rejected	Per-pixel p10/p90/std off the same scenes the median already downloads: +0.0003 AUC, 16 of 30 folds, sign test p=0.57.
Bilinear resampling of the native-20 m bands	shipped	The model's biggest coefficients sit on bands replicated nearest-neighbour from 20 m. De-blocking them: +0.0041 AUC within size band, 20 of 30 folds, p=0.061.
Regression sharpening of the 20 m bands	rejected	Predicting SWIR from the visible bands costs 0.0047 AUC, 7 of 30 folds, p=0.008. The SWIR signal is not synthesisable.
Footprint-constrained spatial unmixing	rejected	Cuts reflectance error 67% on synthetic sub-pixel buildings and loses 0.0323 AUC within size band on real ones, 2 of 30 folds, p=0.000.
Top-of-atmosphere instead of Sen2Cor	rejected	An exact coin flip, 15 of 30 folds, p=1.00, at triple the fold-to-fold spread.
Temporal unmixing	rejected	PV pixels vary as much as PV-free roofs (ratio 1.02), so there is no damping to measure. The gain tracks background dynamism as predicted, at 0.0015 AUC.
Gradient boosting instead of the linear model	partial	Ranks WORSE (-0.023 within size band, 4 of 30 folds) and calibrates BETTER (per-quadrat rate error 0.031 to 0.017, 20 of 29 quadrats).
Footprint shape as model features	rejected	-0.0001 AUC, 14 of 30 folds, p=1.00. The 0.8593 that made it look best was a difference of medians.
Area-weighted training loss	rejected	Improves the area ratio it optimises (1.084 to 1.035) and loses on both ranking (-0.0111 AUC) and count calibration (0.0308 to 0.0395).
A pre-boom epoch as the roof's own control	partial	+0.0234 within size band over the SHIPPED read path (25 of 29 folds, p = 0.0002, placebo null) but only +0.0067 over the noise-reduced one (23 of 29, p = 0.002). The two levers are not additive, and the old epoch is not yet deep enough.
Within-footprint pixel distribution	rejected	Extremes and spread of the same pixels the zonal mean averages: -0.0006 to -0.0015 within size band, 11 to 12 of 29 folds, p = 0.27 to 0.46.
Per-stratum deployment thresholds	rejected	Equal precision per size/density stratum COSTS 3.0 to 9.2 points of recall at matched precision (4 to 7 of 25 quadrats, p = 0.001 to 0.029).
Per-stratum score recalibration, one global cut	rejected	The theoretically-correct version. Pooled +1.3 points of recall, median per-quadrat +0.0000, 12 of 24 quadrats, p = 1.00.
Post-hoc recalibration of roofclf	rejected	Recovers 38% of gradient boosting's calibration gain at p=0.069. No monotone map reaches it, so that gain is a reordering.
Medoid instead of band-wise median compositing	rejected	A real artifact, but one date's spectrum loses more to noise than it gains in coherence: -0.0074 AUC.
Context-relative spectral features	partial	Within-cell z-scores alone TIE the shipped model's ranking while doubling its rate error: ranking is relative, calibration is absolute.
Local-contrast brightness (plus_local_contrast)	rejected	Unmeasurable since 2026-08-09 through a NaN bug; fixed and measured at +0.0000 AUC, 13 of 30 folds.
Cell-level brightness as a feature	rejected	Predicted to help calibration; makes it significantly WORSE, closer in 7 of 29 quadrats, p=0.008.
Snow-covered roofs in Germany	partial	Answers France's opportunity objection at a 10.3% scene rate; the contrast itself is 6 scenes, bimodal, p=0.39.
The composite reducer: mean, not median	shipped-pending	+0.0286 AUC within size band, 25 of 30 folds, p=0.0001. Larger than every feature block in this register combined.
Multi-frame super-resolution	rejected	Sub-pixel diversity is 1.3 m, a quarter of what fusion needs; applying the shifts is WORSE than not.
SEN2SR single-image super-resolution	rejected	-0.0367 AUC within size band, 6 of 30 folds. Invented texture dilutes real contrast.
GlobalBuildingAtlas building height	rejected	d'=+0.651 alone and +0.0006 on top of the model: height is a proxy for footprint area.
The spectral SNR budget	shipped	Measured, not argued: the noise is roof heterogeneity at 20-40x the sensor's, and the linear spectral limit is already reached.
Local background conditioning	rejected	Cuts noise 23-43% and signal faster, at every scale from 31 m to 369 m, because PV adoption is spatially clustered.
Glint geometry as a scene-level SNR lever	rejected	The apparent gain is solar elevation: partialling it out leaves +0.025 (p=0.70).
Compositing in a high-sun window	rejected	Roofs are 21% brighter pre-monsoon and the contrast is unchanged (0.98x): d-prime is illumination-invariant.
Yard features for small ground-mount	partial	The building index brackets 98.5% of the population, but detection lands at 1-2% precision.
Yard-SPPI and roofclf AND-gate for ground-mount	rejected	The rooftop floor's construction does not transfer: 2% precision, and the best operating point turns the roofclf side off.
Parcel label for roofclf	shipped	Counting PV in the yard, not just on the roof. 80% of what it recovers turns out to be rooftop PV overhanging an undersized footprint, not ground-mount.
Yard feature block in the roofclf model	rejected	Loses to roof-only features even against the parcel label it was built for: 0.8712 against 0.8734.
SPPI as a roofclf input feature	rejected	0.8736 to 0.8734 AUC. It helps by disagreeing, not by being a column.
Glint-date imagery as a roofclf feature	rejected	Only 7-24% of rooftops can ever glint into a near-nadir view; size-controlled AUC moves 0.7875 to 0.7879.
Opportunity-normalised glint sensitivity	shipped	Sensitivity varies ~2x with opportunity inside a size bin; now modelled per target instead of pooled.
Glint-mined roofclf hard negatives	rejected	126 negatives at 1.2% contamination moved held-out AUC by 0.0003. Quantity, not quality, is the binding constraint.
OSM as a complete reference in Germany	rejected	3.6% complete by unit count; implied kWp/m² unstable by more than the constant itself.
External corroboration: nightlights and a wealth index	partial	VIIRS nightlights and Meta's Relative Wealth Index correlate with published capacity (r=0.76, r=0.66), but partial correlation shows most of that tracks the same building-density confound (0.54, 0.35). Kept as a plausibility citation, not promoted to a stratification input.
Unrestricted national roofclf capacity	superseded	18 to 37 GWp, rejected as miscalibrated; replaced by the domain-restricted estimate.
Low/Central/High/All-PV bracket atlas	superseded	Replaced by the two-tier evidence atlas, which sorts by standard of proof instead.
Out-of-domain AND-gate extrapolation	superseded	Published 2026-08-11 to 2026-08-15, then dropped: the one Best-estimate component not measured where it was applied, and unreviewable by eye under stale imagery. -62 MWp.
The Ceiling tier	superseded	A threshold change roughly doubled it with no new validation, so it stopped bounding anything.
National dashboard bundle	superseded	Replaced by plain per-artifact result pages; the CLI still works.

What worked, and why

In-domain training chips

Training on Germany alone gave 0.18 per-installation recall at or above 1,000 m² in Punjab. Adding 274 Punjabi chips, merged with scripts/merge_chip_index.py and oversampled twice so Germany's larger chip count does not swamp them, took that to 0.55. Nothing else tried has moved the domain gap comparably, which is the empirical argument for the mapping flywheel: more verified Pakistani installations are worth more than any architectural change tested here.

A per-building classifier instead of a better segmenter

The decisive finding of this project is that below roughly 400 m² the question has to change. Segmentation asks "where is the polygon", and at 10 m ground sampling distance a 50 m² rooftop array does not have one. roofclf asks "does this building carry PV", which turns an unresolvable localisation problem into a per-footprint classification problem with a strong size prior and reflectance features. On the 27 mapped quadrats it reaches 0.879 AUC, and 0.834 with roof size controlled for, where the segmentation raster scores close to chance on the same buildings. That gap, not any architectural change to the segmenter, is why the pipeline now runs two detectors.

Requiring two detectors to agree

SPPI is a zero-training spectral index from the literature. As a standalone capacity instrument it is unusable (an 18x scale spread across quadrats, 4.7x over-prediction in arid Quetta), and as an extra column in roofclf it does nothing at all (0.8736 to 0.8734 AUC). What it is good for is disagreeing: requiring roofclf and SPPI to agree lifts precision from 0.496 to 0.540 at matched recall, and the gain concentrates in exactly the low-adoption places where roofclf alone is known to over-predict. That is why agreement between the two sets the internal floor under the evidence atlas's headline figure, rather than either model on its own.

One estimator, applied to both halves

The segmentation half already corrects for missed installations: each detection represents 1/recall real installations within its size class. The roofclf half did not. Its coverage ratio was measured only over flagged roofs, effectively assigning zero capacity to PV on missed roofs, even though roofclf contributed roughly four-fifths of the published estimate.

Using the existing labels from 16 trusted quadrats, area recall was estimated as the share of mapped PV area on buildings flagged by roofclf, by roof-size bin and building-density stratum. It is 0.808 below 400 m² and 0.978 at or above 400 m², rising from 0.34 for the smallest PV-carrying roofs to 0.99 for the largest. Area recall is used because missing a 300 m² array matters far more to capacity than missing a 20 m² one.

The correction raised the two roofclf components from 6,372 to 7,890 MWp and from 7,031 to 7,189 MWp, increasing the published Best estimate from 16,609 to 18,280 MWp. The internal floor was left unchanged because it is defined by agreement between two independent detectors and should not extrapolate to installations neither observed.

Coverage ratio and recall are refit within the same bootstrap replicates because they share the same quadrats and labels. The correction also remains conservative, chiefly because mapping completeness is certified only for the imagery epoch, so some recent installations on unflagged roofs can still be missed.

The vegetation veto, and why the obvious version fails

Manual review of countryside leads found many green fields flagged as PV. Measuring NDVI on the composite the model actually read does not separate them: 150 suspect leads had a median NDVI of 0.10 there, statistically indistinguishable from confirmed PV's 0.04. The field a validator sees as green today was dark fallow, harvested or flooded paddy soil when the dry-season median was built. This is a season mismatch, not a spectral confusion the model could have avoided.

The instrument that does discriminate is the annual vegetation cycle: every crop field greens up at some point in the year and a panel never does. scripts/veg_annual_ndvi.py samples a year of scenes per lead and reports the 95^th percentile. A positive control on ten leads the free version had already flagged found eight crossing 0.3 within the year, confirming the catches are real vegetation.

External corroboration from nightlights and a wealth index

Two proxies that had never appeared anywhere in this codebase before -- VIIRS nighttime-lights radiance and Meta's Relative Wealth Index -- were correlated against the published atlas's per-cell Best estimate to ask a narrower question than validation: does detected PV track the same "built-up, electrified, and (for RWI) relatively wealthy" signal an independent, non-imagery-derived dataset would predict. Neither proxy sees solar panels; agreement is evidence the estimate is plausible, not evidence it is correct, and it is cited that way in Validity and limitations, not used as a calibration input.

scripts/build_pv_external_comparison.py computes log(1+radiance) vs. log(1+MWp) and, separately, RWI vs. log(1+MWp), per 0.1° cell, plus the same correlation after partialling out each cell's building footprint area (roof_area_m2) -- the obvious shared confound, since a bigger built-up cell is both brighter and has more roof to put PV on. It had run once, 2026-08-12, and was never registered here. Rerun 2026-08-26 against the current 18,826.7 MWp atlas (14% higher than the 16,441.4 MWp the first run saw): nightlights correlate at Pearson 0.761 raw, dropping to 0.545 once roof area is controlled for (n=4,463 cells); the wealth index at 0.661 raw, dropping to 0.345 (4,428 of 4,463 cells have RWI coverage). Both partial correlations barely moved between the two runs (0.548 to 0.545 for nightlights, 0.373 to 0.345 for RWI) despite the 14% shift in the headline total, so the relationship looks like a property of the estimate's geography rather than an artifact of one particular refit.

The honest reading is that both proxies carry some information about PV placement beyond "this cell has more buildings" -- the partial correlations are positive and well clear of zero -- but a majority share of the raw correlation is exactly that confound, not new information. That is consistent with Capacity density's own three failed attempts at finding any coarse per-cell proxy (candidate density, roofclf's predicted rate, SPPI agreement rate) for which quadrat-regime a cell resembles: neither nightlights nor RWI has been tried in that specific role, and this result is why they are not expected to do much better than the three proxies that already failed there (see Open questions, item 15). The same script also recomputes the reference-hex comparison already published on the capacity map via the same grid match, as an internal consistency check rather than a second citation of it.

A legally-complete register as a precision instrument (2026-09-01)

Germany's own accuracy check had a hole in it: with no instrument for p_unmapped, its calibration table shipped 0.0, pricing every un-mapped candidate at zero and holding est_mwp_cal to an OLS slope of 0.038 against the register.

MaStR closes it over part of the range. Coordinates are published only at or above 30 kWp — zero of the 4.17 million units below that carry one, a privacy policy rather than missing data, while the fill rate is 99.7% above the 72 kWp / 400 m² segmentation floor. So a registered installation's address point falling inside an unmapped candidate is direct evidence that the candidate is real, for exactly the population segmentation targets. Measured per placement, the term runs from 0.061 to 0.688 for rooftop and 0.000 to 0.246 for ground, chance-corrected against the same polygons displaced 500-1,000 m.

Two things generalise beyond Germany. The register is silent below 30 kWp, which is the roofclf domain, so this improves the instrument that was already working and does nothing for the one that needed help most — a register does not remove the need for mapped quadrats. And the natural refinement, dividing by an OSM-mapped positive control the way the glint inversion does, fails here: German OSM rooftop PV is the 3.6%-complete enthusiast-mapped tail, which skews below 30 kWp and so carries no coordinates, making the control contaminated by the very suppression it was meant to absorb. Full derivation and the rejected control: Validating against a complete register.

The recall denominator, found by fixing something else (2026-09-02)

Correcting Germany's p_unmapped moved est_mwp_cal the right way and pushed est_mwp_rc_roof from 0.262 to 3.11 against the register — from understating truth to overstating it threefold. Two errors had been cancelling, and removing one exposed the other.

The cause was not either hypothesis first written down. It was that derive_placement_tables restricted both sides of the recall measurement by placement: a rooftop reference installation only counted as found if the finding candidate was itself classified rooftop. Precision and recall are asymmetric here. mapped_frac asks "is this candidate real", so its corroboration must come from references of its own placement. Recall asks "was this real installation detected at all", and how postprocess labelled the candidate that found it is irrelevant.

Measured on Germany, rooftop recall against same-placement candidates versus against any candidate:

Bin	same placement	any candidate	factor
500-1k m²	0.128	0.167	1.3x
1k-5k	0.214	0.268	1.25x
5k-50k	0.096	0.693	7.3x
>50k	0.036	0.852	23.9x

The mechanism explains why the error grew with size: a large array overruns its imagery-derived VIDA footprint, building_overlap_frac collapses, and the candidate that correctly found a rooftop installation is classified ground_adjacent or no_building — the same footprint undersizing the parcel label exists to handle. 1/recall then inflated those candidates by up to the 20x clamp.

Pakistan is affected too, which only a complete register could have revealed: rooftop recall there moves 0.423 to 0.808 in the 5k-50k bin and 0.065 to 0.952 above 50k, and ground 0.107 to 0.417 in 500-1k. Its published figures come from the checked-in calibration table and do not move until that is deliberately re-derived, which needs the glint sample and calibration boxes. Direction: Pakistan's est_mwp_rc is overstated.

Two hypotheses were written down first and both were measured and refuted: that oversize rooftop reference features deflated the top bin (they recall at 0.841, no different from the rest), and that count-recall applied to area inflated the estimator (the area/count ratio is 1.01 to 1.08, negligible). Recorded because the wrong diagnosis was the plausible one.

A second register, and what it could measure that the first could not (2026-09-04)

Germany's register settled the question above the 400 m² floor and was structurally silent below it: MaStR publishes no coordinates under 30 kWp, which is roofclf's entire domain. France's register is censored in the same direction, below 36 kW, but two features of it reach where Germany could not.

It publishes dated year-end vintages. Because a small installation cannot be dated individually inside an aggregate row, the current snapshot cannot be filtered to a past epoch, but a past snapshot can simply be downloaded. Interpolating per-commune small-PV capacity between straddling year-ends puts the reference on the same day as the imagery a label was drawn against. That correction is worth 53%: the same fourteen communes read 0.230 kWp/m² against today's register and 0.150 against the register as it stood when the aerial imagery was flown. The difference is entirely PV France installed in between, and any comparison that skips the step measures growth and reports it as physics.

Fourteen communes were swept exhaustively by hand, on sub-metre imagery, with solar-thermal collectors tagged separately from PV. That makes the register's small-unit aggregate and the mapped sub-200 m² polygons the same population, and dividing one by the other measures DEFAULT_KWP_PER_M2_MODULE externally for the first time. It comes out at 0.150 kWp/m² against the 0.180 the project assumes, ratio 0.83, stable at 0.147 under the widest reasonable definition of what counts as PV. The German attempt at the same measurement failed and was recorded as a negative result: 3.6% OSM completeness and an implied constant spanning 0.02 to 0.99 on mapper convention.

Two findings fell out of it that were not the point of the exercise.

The below-floor share is not transferable, now measured twice rather than argued once. Germany's page inferred that from dispersion across municipalities. France settles it: 17.3% of all registered French PV capacity sits below 72 kWp, 31.1% of low-voltage-connected capacity, against Germany's 65.5% of rooftop. France's fleet is far more ground-mount-heavy, so the practical cost of the floor is genuinely smaller there. Two complete registers differ by a factor of two to four on the quantity this project once transferred as a constant.

Solar thermal is a systematic confusion for imagery-based detection, and it is invisible without the tag. 381 of 3,335 mapped features are hot-water collectors: black glazed rectangles on south-facing roofs producing no electricity. 137 OpenPVMapper polygons in these communes sit on one. Any validation whose ground truth does not separate them books that as a detection error or, worse, as capacity.

France also cannot do one thing Germany can. Its register carries no coordinates at any size, so the p_unmapped precision instrument has no French counterpart, and no rooftop/ground attribute, so every share is bracketed rather than stated.

Where the sub-400 m² instrument stops working (2026-09-04)

roofclf is the half of this project that has no external check, so France was the place to get one. It did not survive it.

Fitted on 13 exhaustively hand-mapped French communes (44,314 buildings, 1,231 with PV, leave-one-quadrat-out, parcel label), it reaches median fold AUC 0.710 and 0.627 within size band, against Pakistan's 0.857 and roughly 0.834. Building footprint size alone gets 0.673, so spectral reflectance is worth about 0.037 AUC here. At a threshold tuned for 50% precision it flags 16 buildings out of 44,314. Segmentation scores exactly 0.500 in all 13 folds.

The cause is not the model. The median mapped French installation is 20 m² against a 100 m² Sentinel-2 pixel, so the array is a fifth of a mixed pixel that is mostly roof tile. The control that isolates this is OpenPVMapper, which recalls the same installations at 0.67 from sub-metre aerial imagery with no size gradient across 0 to 400 m²: the annotations are findable, the sensor cannot find them.

This bounds roofclf rather than refuting it. Pakistani quadrats contain installations that are small relative to the 400 m² segmentation floor but still large relative to a 10 m pixel. French residential PV sits below even that, and the instrument degrades to little more than a size prior there. The practical consequence for the published Pakistani atlas is a caution, not a correction: the sparse-density stratum, already the least well-supported part of the coverage-ratio fit, is the one nearest this regime.

It also answers, negatively, the "independent test of the 400 m² floor" that open question 4 asked for. The floor is real, it is a sensor limit, and below roughly a pixel of module area there is no Sentinel-2 instrument here at all.

Localizing the detector: what French chips bought (2026-09-12)

France was the first country this project inferred zero-shot -- the v4 checkpoint was trained on Germany, Pakistan, Punjab and Gujarat and had never seen a French roof -- and the first where a complete register could score that decision both before and after retraining. Same 20,501 communes, same 93.0% capacity coverage, so the two runs are directly comparable.

estimator	zero-shot slope	retrained slope	zero-shot ρ	retrained ρ	Germany slope	Germany ρ
est_mwp_det	0.127	0.160	0.262	0.446	0.340	0.661
est_mwp_exp	0.162	0.170	0.290	0.449	0.388	0.656

The gain is in placement, not magnitude. Spearman rose 55% and closed about half the gap to in-domain Germany; slope moved 5 to 25% and total recovered capacity went from 26% to 34% of registered above-floor low-voltage capacity. Spearman is scale-free, so unlike slope it cannot be explained away by conversion constants or by France's proxy denominator (the register has no rooftop/ground field, so "above floor, low voltage" still contains BT-connected ground-mount). The zero-shot model was putting capacity in the wrong communes; the retrained one largely is not.

What changed underneath is more interesting than the totals. The candidate population did not simply grow, it changed shape: 13,419 candidates became 39,462, the median candidate shrank from 8,301 to 1,400 m², rooftop share doubled from 26.9% to 50.3%, blobs at or above 10,000 m² fell from 6,036 to 2,076, and total detected area went down, 364 to 255 km². polygonize_chips merges touching thresholded pixels with no upper bound, so a sheet of weak false positives becomes one multi-hectare "installation"; a zero-shot model tuned on Pakistani and German industrial roofs produced exactly that over France. Three times as many objects, six times smaller, with a third less total area, is the signature of a detector matched to a fleet of small rooftop arrays.

A prediction recorded before the baseline ran was half right and worth keeping as calibration on this kind of forecast: the slope band (0.15 to 0.35) was about right for the uncalibrated estimators but anchored on the wrong comparator, and the rank-correlation collapse -- flagged in advance as "the more interesting failure" -- was the thing that actually happened, at 0.29 against Germany's 0.66.

The caveat, stated because it limits the claim. France is 73.6% of the retrained corpus (18,577 chips against 6,661 from every other region combined), so this result confounds "French data is present" with "the corpus is 3.8x larger". A France-capped run of about 3,200 chips, matching Germany's share, would separate them and has not been run. The checkpoint (v5_combined_france, epoch 25, early-stopped at 33) is applied to France only; Pakistan and Germany keep v4 and their published figures are unaffected.

The detection floor, measured against sub-metre truth (2026-09-12)

The 400 m² floor was always an argument from the sensor, four Sentinel-2 pixels, checked against quadrats labelled off the same class of imagery. The fourteen French communes are swept on sub-metre IGN orthophotos, so for the first time a missed installation is provably a miss rather than an annotation gap. mapped_vs_earthpv measures recall per size bin against them.

Recall climbs monotonically with installation size, Spearman +0.83 (p = 0.042), from 0.000 below 20 m² to 0.095 in the 200 to 400 m² bin. OpenPVMapper, reading the same installations inside the same boundaries from sub-metre imagery, is flat: -0.29 (p = 0.58), recalling 0.58 to 0.80 in every bin. That contrast is the whole point of the measurement. A gradient present in a 10 m detector and absent in a sub-metre one, against identical truth, is the sensor rather than the mapper.

The finding survives retraining, across three models. v4 zero-shot (no French chips), v6 (3,201) and v5 (17,059) return pooled count recall of 0.0118, 0.0103 and 0.0118, with gradients of +0.89, +0.89 and +0.83, even though v5 tripled the national candidate count and cut median candidate area from 8,301 to 1,400 m². In these communes only 28 candidates appear at all, and the median distance from a 400 m²-plus mapped installation to the nearest one is 807 m.

This corroborates roofclf's French result from the segmentation side, by a fully independent route: the classifier found no signal in the pixels, and the segmenter finds no polygons in the same places.

What it does not settle. The 400 m²-plus bin holds 44 installations, recall 0.045 with a 95% Wilson interval of 0.013 to 0.151, far too thin to claim EarthPV fails above its own floor. These communes were chosen to be exhaustively mappable, not to hold large arrays. The transferable result is the gradient against the control, not the level in any one bin, and it bounds the sensor rather than the method: Pakistani arrays are small against the 400 m² floor but still large against a 100 m² pixel.

Authoritative footprints: the French cadastre against VIDA (2026-09-12)

roofclf reads VIDA Open Buildings, which is imagery-derived. That is the right choice where no authoritative footprint layer exists, which is the case across most of the project's target geography, but it is not free. Measured over the fourteen hand-mapped French communes against the DGFiP cadastre published through Etalab:

	Buildings	PV-bearing	Median footprint	AUC	Within size band
VIDA	44,314	1,231	163 m²	0.7103	0.6274
Cadastre	103,697	1,759	53 m²	0.7369	0.6548

VIDA finds 43% of the buildings and 70% of the PV-bearing ones. In Toussieu specifically it returns 672 footprints where the cadastre returns 3,366, with a median of 146 m² against 22 m², and 1.9x less total built area. Bad footprints degrade three things at once: the roof-area feature, which is the single strongest predictor; the zonal spectral means, which get averaged over the wrong pixels; and the label itself, which is mapped PV intersected with the footprint.

Fixing all three is worth +0.027 AUC within size band, closing 14% of the gap to Pakistan's 0.821. Real, worth adopting for future French work, and not remotely enough to make the instrument deployable. The other 86% is the sensor.

Two operational findings came out of this. Overture carries essentially the same French footprints (3,826 in the Toussieu bbox against the cadastre's 3,366 inside the boundary) but costs a global S3 scan at roughly 166 seconds per commune against the cadastre's two, and Overture prunes its release directory to the last two releases, so configs/aoi.yaml's pinned 2026-06-17.0 no longer exists and fails as "No files found", which reads like an empty area rather than an expired release. The cadastre is published per commune keyed by INSEE, which is already the unit the French quadrats use.

building_table gained an optional buildings override for this; the default keeps the VIDA fetch, so no existing caller changed.

What did not work

Two-season stacking

A 20-band two-season stack (dry-season base plus a contrast season per cell) was built to push detection below 1,000 m², on the theory that PV is spectrally stable across seasons while vegetation and roofs swing. The full path is wired and TerraMind duplicates its pretrained patch embedding into both season slots.

On the same Punjab validation installations, recall for the 1,000+, 500 to 1,000 and 250 to 500 m² buckets was 0.51, 0.17 and 0.14 seasonal, against 0.55, 0.16 and 0.14 for the production 10-band model. Small buckets unchanged within noise, large slightly worse. Likely causes: too few in-domain chips to learn a temporal signal, the tiny backbone's capacity, and post-monsoon versus dry season simply not differing enough in arid Pakistan. A later variant using a 2021 pre-boom epoch instead of a contrast season (boom-window stacking) was inconclusive rather than negative: training collapsed on only 332 usable chips, so it did not test the hypothesis either way.

Sentinel-1 corner reflection

A tilted PV row over flat ground forms a dihedral corner reflector, which should produce strong radar backscatter, persistently, since Sentinel-1's orbit geometry is fixed year-round and radar is not blocked by cloud. Tested on 17 glint-validated installations spanning the full observed azimuth range, pulling two years of Sentinel-1 RTC.

Median enhancement rate was 3.2% (VV) and 1.7% (VH) of scenes, within plain speckle noise. Critically, ascending and descending passes gave near-identical rates, 1.7 against 1.8%, with no correlation to the implied row axis. A real corner reflector should show sharp asymmetry between orbit headings, and its absence says this is not a usable channel at these sites through a simple per-footprint aggregate.

A lighter use of Sentinel-1 remains untried and is not ruled out by this: multi-temporal backscatter variance separates permanent structures from seasonally changing fields, and greenhouse metal frames give a bright return, the opposite of PV, making radar a cheap post-hoc false-positive filter.

Two glint routes to density, both negative

1. Cell-level spike counting

The idea was that a dense neighbourhood of sub-pixel residential arrays might produce more cell-level glint spikes, even when individual arrays rarely glint strongly enough to detect alone. This was tested on a fully mapped Lahore cluster with up to 120 installations within a 300 m block.

• Zero-PV controls: 1.0 mean spike
• PV-bearing cells: 1.45 mean spikes
• Median for both: 1.0
• The 120-installation hotspot produced only one spike in two years

The signal was too weak to distinguish PV-rich cells reliably. This likely reflects the statistic rather than the underlying glint physics, since a 90^th-percentile cell measure only responds when a large fraction of the cell brightens at once.

2. Recovering missed installations

A second approach tested whether glint could validate real PV installations missed entirely by the segmentation mask, allowing their area to be added back. It was evaluated on 43 missed German installations and 208 missed Lahore installations, each compared with matched non-PV controls.

• Germany: 37.2% validation on missed PV vs 20.8% on controls
• Lahore: 5.3% validation on missed PV vs 8.7% on controls

The separation is not strong enough for reliable recovery. In Germany, the control false-validation rate remains high, while in Lahore the control rate is actually higher than the missed-PV rate, indicating no useful discriminative signal.

A measured glint PSF, and the control set that mattered more (2026-08-17)

The astronomical version of the sub-pixel problem: measure the instrument's point-spread function from strongly-validated glints, then matched-filter against it, the way stellar photometry pulls faint sources out of noise. Full write-up: Glint PSF and matched filtering.

The PSF part works. Fitting a forward model (polygon rasterised at 1 m, Gaussian-blurred, block-averaged onto each scene's own grid at the target's true sub-pixel position) over 68 targets below 500 m² gives sigma 0.65 px, 90% CI 0.60 to 0.70, against an optical theory range of 0.49 to 0.62 px implied by ESA's stated MTF. The residual excess is covered by a measured per-scene source displacement of 0.72 px median, so the fitted kernel is an effective one that already contains co-registration.

The detector part does not. Every variant tested, centroid-pinned, offset-fitted, and point-source, scores below the p98-minus-annulus statistic already in the pipeline (AUC 0.485 to 0.620 against 0.648 to 0.655), significantly so over all sizes. The reason is visible in the fit itself: sigma climbs from 0.65 to 2.20 px with installation area while variance explained collapses from 0.49 to 0.06, because a specular glint comes from whichever patch satisfies the mirror condition on that date rather than from the whole array. Matched filtering is optimal for a known shape at a known position and a glint has neither, which is the one property stars have that makes the analogy work.

The valuable result was the control set built to test it. 600 verified negatives, buildings inside the 27 Rule-1 complete quadrats carrying has_pv == 0, give a false-spike rate of 2.0%, against the 8.7 to 20.3% previously measured on merely model-negative controls. An ablation puts the per-pixel SCL cloud veto at half of that improvement (4.5% with it disabled) and unmapped real PV in the old controls at the rest. Against true detection rates the instrument separates by 15x at 100 to 500 m² and 7.9x below 100 m², so the spike-rate density estimator's stated blocker, that false rate equals or exceeds true rate below 500 m², does not hold against verified negatives.

Super-resolution

Three feasibility tests, run in sequence by scripts/run_sr_experiments.sh: guided fusion of the 20 m bands to 10 m, multi-image super-resolution from repeated overpasses, and internal-learning single-image super-resolution. None improved detection, and the last carries an obvious hallucination risk on a task whose whole output is "is there a panel here". Scripts are kept for reference.

Choosing the imagery date so panels glint

A well-motivated idea that the geometry refuses. roofclf reads a dry-season median composite, and a median is built to suppress exactly the transient specular events that mark a panel, so reading the dates when panels should glint ought to raise the signal-to-noise ratio, especially in dense blocks where many would brighten at once.

Measured two ways. First the ceiling, from real granule sun and view angles over two years at all 23 quadrats: because Sentinel-2 views near-nadir, the pose that reflects sunlight into the sensor is a narrow locus, and only a median 13.2% of a plausible south-facing installed population (range 6.7 to 23.6%) can land on it at all. A textbook south-facing array at tilt 30 misses by 8.6 degrees on every scene in the archive. The single best date reaches 1.0 to 1.8% of rooftops, so the "one optimal date" framing fails specifically.

Then the feature itself, on the Lahore quadrat (13,500 buildings, 3,432 with mapped PV, the densest ground truth here and exactly the dense-urban case the idea targets). It separates PV standalone at 0.613 AUC, but 0.528 within roof-size band, i.e. nearly all of that was size. Added to roofclf's own features under a spatial holdout, size-controlled AUC moves 0.7875 to 0.7879.

Full derivation, the pose-window figure and before/after imagery of the best-case buildings: Solar glint. Two narrower versions survive untouched: per-locality pose calibration, and glint's existing role corroborating individual large arrays.

Snow-cover contrast, and the one commune that nearly sold it (2026-09-08)

The physics here is better than the glint idea's, and it targets a weakness this project can name precisely. Panels are tilted, smooth and dark, so they shed and melt snow faster than the roof around them: under lying snow a small array stops being a dark patch on a dark slate roof and becomes a dark patch on a white one. At 10 m, for a 20 m² array in a 100 m² pixel, that is the difference between a 8% reflectance deficit against a slate roof, inside BRDF noise, and a 19% deficit against snow on a brighter, higher-SNR background -- roughly 2.4x the signal, and aimed squarely at the dark-roof contrast problem that makes French rooftops harder than Pakistani ones.

It fails on opportunity, which is the same thing that killed choosing the imagery date so panels glint, and it was measured the same way: not by building a detector, but by counting how many chances a roof actually gets. scripts/snow_opportunity.py reads SCL on its native 20 m grid inside a rasterised mask of each commune's VIDA footprints -- snow over farmland says nothing about whether roofs were white -- and counts a scene as an opportunity when snow exceeds 5% of roof pixels while cloud stays under 20% of them. Both conditions matter, and the cloud one is easy to forget: in France a snowfall usually arrives with the system that hides it.

Across 1,556 scenes, 14 communes and four winters (2021/22 to 2024/25): 84 opportunities, of which 79 are in a single commune. Saint-Martin-de-la-Porte, alpine Maurienne at roughly 1,000 m, returns 19.8 per winter and peaks at 99.7% roof snow. Chambery returns 0.50 per winter, Grenoble, Langoat and Saulny 0.25 each, and nine of the fourteen communes return zero in four winters. Excluding the one alpine commune leaves 0.096 opportunities per commune-winter, about one usable scene per commune per decade.

Three things then compound, and the first is decisive:

• The signal is where the fleet is not. On a deliberately generous definition of mountain departements (Alps, high Pyrenees, Jura, Vosges, Massif Central), only 10.8% of France's 34.6 GWp of registered PV sits there.
• It would need a retrain, not a processing change. Snow currently causes false positives here: growth.py records 91 of 155 km² of spurious "vanishing" PV above latitude 34 as winter snow, and imagery.py excludes SCL class 11 from composites by design. The contrast could not be harvested by the current model, only by one trained on snow-bearing labels that do not exist.
• Winter geometry adds a false-positive mode. At 45 N in December the sun sits near 20 degrees elevation, so shadows become large relative to buildings at 10 m, precisely while the method hunts dark patches on bright roofs.

The methodological lesson is about the pilot, not the physics. The alpine commune was run first as a smoke test and on its own it argued the opposite conclusion, roughly 20 opportunities per winter. It is the outlier, not the norm, and a single-site pilot would have justified building the whole thing. Opportunity has to be counted over the population the method would be applied to.

One variant survives and is worth stating: Germany, which has more reliable lowland snow and is already the largest region in the training corpus, so the retrain objection is weaker there. If the snow hypothesis is ever tested properly it should be tested there, not in France. Raw counts: results/france_snow_opportunity.csv.

The 20 m bands, and what sharpening them costs (2026-09-19)

Six of the ten composite bands are 20 m at the sensor: B05, B06, B07, B8A, B11 and B12. The composite is written at 10 m, and odc.stac was replicating each 20 m value nearest-neighbour into a 2x2 block. Measured on cell 0122_0077, B11 and B12 are constant across 2x2 blocks in 100.0% of the raster, against 0.0% for B02 and B08.

That matters more than it sounds, because of where the classifier's weight sits:

Coefficient	Value	Native
b11_mean	+4.33	20 m
swir_vis_ratio	-3.92	uses SWIR
ndvi	+3.73	10 m
ndbi	+3.65	uses SWIR
b12_mean	-3.23	20 m
b02_mean	+2.78	10 m

The four largest are SWIR or SWIR-derived. So the model leans hardest on bands that carry no independent information at the grid it is scored on, and a 100 m² building's SWIR value comes from a 20 m cell whose centre can be 10 m away from it.

Two fixes were measured against the 30 production quadrats, leave-one-quadrat-out, paired per fold, changing nothing but how the same downloaded pixels reach the 10 m grid:

Variant	AUC	Within size band	Folds better	Sign test
Baseline (nearest)	0.8575	0.8206	--	--
Bilinear from the true 20 m grid	0.8574	0.8322	20 of 30	p = 0.061
Regression sharpening	0.8570	0.8060	7 of 30	p = 0.008

They point in opposite directions, and that is the finding. Regression sharpening predicts each 20 m band from the four native-10 m bands and adds back the residual, and it hurts significantly: -0.0047 AUC, -0.0052 within size band, better in only 7 of 30 folds. The interpretation is the useful part. If SWIR were largely a function of the visible bands, synthesising its 10 m structure from them would be nearly free; that it costs skill says the SWIR signal the classifier uses is genuinely independent of the visible bands, and replacing real-but-coarse SWIR with a visible-derived estimate throws it away. This is a property of PV worth remembering: it is spectrally distinctive in the shortwave infrared precisely where the visible bands cannot see it coming.

Simply removing the blockiness, by taking each band back to its true 20 m grid and resampling bilinearly, moves the other way: +0.0041 AUC within size band, better in 20 of 30 folds, p = 0.061, with the median within-size AUC going 0.8206 to 0.8322. That is suggestive rather than established, and it is free. It says the defect worth fixing is the misregistration, not the missing detail.

Shipped as imagery.BAND_RESAMPLING / annual_composite(resampling=...), default "20m-bilinear", with --resampling nearest to reproduce the old behaviour. Every composite records which it used in an earthpv_resampling tag; a composite with no such tag predates this and is nearest. The two must not be mixed within one AOI: the change is small but systematic, and a model calibrated on nearest composites and scored on bilinear ones is a domain shift rather than an improvement. That is enforced rather than documented -- compose inherits an AOI's existing mode and gives only a fresh AOI the new default, because a country-scale run is a restart loop and the first pass after a default changes is where silent mixing happens. Pakistan, Germany, France, Zambia and Nigeria (20,160 cells between them) are all nearest today and stay that way until someone recomposes one wholesale, which on p=0.061 evidence is not worth the bandwidth. SCL keeps nearest regardless, because interpolating between class 4 and class 8 invents class 6.

Unmixing the pixel against a known footprint (2026-09-19)

A zonal mean over a building's pixels is a mean of MIXTURES: a 100 m² roof is one 10 m pixel it shares with road, yard and neighbours. That dilution is the mechanism behind nearly every negative result in this register. Blind two-endmember unmixing was rejected at 0.659 AUC, but this is a different problem and a better-posed one, because the abundances are known: the footprints say what fraction of each pixel belongs to which building, so

y_p = sum_j A_pj r_j + (1 - sum_j A_pj) b_p

is a linear inverse problem for the per-building reflectance r, solved for a whole quadrat at once with a ridge toward the zonal mean (preprocess.unmix_buildings).

On synthetic data it works exactly as intended. With buildings deliberately offset from the pixel grid and 36 to 196 m² in size, so the median building fills 38% of its brightest pixel, unmixing cuts mean reflectance error against truth by 67% (0.0975 to 0.0319) and recovers 84% of the true dark/bright contrast where the zonal mean recovers 28%.

On real quadrats it is the worst result in this register: -0.0205 AUC and -0.0323 within size band, better in 2 of 30 folds, sign test p = 0.000. Not noise, not a tie, a substantial loss.

Two explanations, and the second is the interesting one:

• The footprints are not that good. The solve assumes A is exact. VIDA is imagery-derived and systematically undersized -- this project already measured 117,003 m² of mapped rooftop PV overhanging VIDA outlines across 27 quadrats, which is 80% of what the parcel label recovers. A wrong A does not merely fail to help, it attributes the wrong pixels to the building.
• The mixture is not purely a nuisance. The parcel label shipped because PV beside a building counts toward it, and the yard carries label-correlated signal. Unmixing does the exact opposite: it strips the surroundings out in order to isolate the roof. Running it against a parcel label is close to self-defeating, and the size of the loss suggests the context is worth more than the purity.

The honest summary is that dilution is real but removing it is not the same as recovering the signal, and an instrument that assumes its geometry is exact inherits every error in that geometry. Kept as roof-classifier-side machinery behind building_table(preprocess="unmix") for anyone who arrives with authoritative footprints -- the French cadastre is the obvious candidate, since it already bought +0.027 AUC within size band over VIDA there -- but off, and not recommended on an imagery-derived layer.

Raw numbers for both: results/roofclf_preprocess_ablation.json and results/roofclf_preprocess_*_folds.csv.

L1C against L2A: the correction is not the problem (2026-09-19)

Sen2Cor's surface-reflectance retrieval assumes a Lambertian surface and constrains aerosol from dark targets. A PV module is specular AND dark, so on the face of it L2A models the wrong physics over exactly the target of interest, in a geometry-dependent way, and it clips the saturation a real glint produces. If that costs anything, the fix is free: use L1C top-of-atmosphere reflectance instead.

Getting the data was the awkward part and is worth recording. Planetary Computer publishes L2A only, and Earth Search's sentinel-2-l1c assets point at ESA's requester-pays s3://sentinel-s2-l1c, which needs AWS credentials. Google's gcp-public-data-sentinel-2 mirrors the same SAFE archives and is anonymously readable, so imagery.l1c_composite runs discovery on STAC and reads pixels as JP2 over HTTPS from GCS, on the parent composite's own pixel grid. L1C carries no usable cloud mask (QA60 is empty from baseline 04.00), so the mask comes from the L2A scenes of the same solar days. Serially this costs about 12 minutes a quadrat, nearly all of it JP2 opens; a thread pool over the 120 (scene, band) reads brings it to 108 s.

The composite validates against L2A exactly as atmospheric physics requires: TOA/BOA is 1.42 in the blue, 1.19 green, 1.09 red, 0.97 NIR, 0.93 and 0.89 in the SWIR -- strong Rayleigh path radiance at short wavelengths giving way to absorption at long ones.

And it makes no difference. Over the same 30 quadrats, same rows, same features:

	AUC	Within size band
L2A (shipped)	0.8575	0.8206
L1C	0.8429	0.8102
Paired delta	+0.0004, 15 of 30 folds, p = 1.00	-0.0039, 12 of 30, p = 0.46

Fifteen folds each way is as null as a result can be, so the Lambertian objection, while physically real, costs the classifier nothing measurable.

The interesting part is the spread rather than the centre. The paired median is zero while the median AUC drops 0.0146, and the fold-to-fold IQR is about three times the other variants tested here (-0.0274 to +0.0128 within size band, against -0.0007 to +0.008 for bilinear resampling). A few quadrats get much worse under L1C while the typical one is unchanged. That is what an absent atmospheric correction should look like in a leave-one-quadrat-out design: path radiance varies with aerosol and geometry BETWEEN quadrats, so removing the correction adds cross-quadrat variance without touching per-building discrimination. The correction earns its place in transferability, not in contrast -- which is precisely the property a model trained on some quadrats and applied to a whole country depends on.

Kept as building_table(preprocess="l1c") plus scripts/compose_l1c_quadrats.py, because the reader is the reusable part: it is the project's only route to TOA reflectance, and glint work has a standing interest in saturation that L2A discards.

Temporal unmixing: the dry-season window removes its own signal (2026-09-19)

The third and last unmixing attempt, and the only one that needs neither the footprint nor a known endmember spectrum. Its constraint is that a PV array's AREAL FRACTION of a pixel is constant while the background's reflectance is not, so taking the temporal spread of

y_p(t) = f_p . r_PV + (1 - f_p) . b_p(t)

gives spread(y_p) = (1 - f_p) . spread(b_p), and f_p is one minus the ratio of a pixel's temporal spread to its local background's. That is self-normalising against roof colour, where absolute reflectance is not: a dark roof and a panel look alike in a median composite, and the claim is that they move differently through a season.

On synthetic data the estimator is near-unbiased, recovering 0.492 for a true fraction of 0.50 and 0.247 for 0.25, and refusing (100% NaN) when the background is held still.

On real quadrats the premise is simply false. scripts/pv_temporal_invariance.py compares per-pixel temporal MAD inside mapped PV against PV-free roof pixels in the same quadrat, which tests the assumption directly and needs no classifier:

	Median across 22 quadrats	Range
PV pixels / PV-free roof pixels	1.02	0.89-1.23
Open background / roof pixels	1.12	0.91-2.02

A PV pixel varies over time exactly as much as the roof beside it, so there is no damping to measure; and the open background moves only 12% more than a roof, so there is barely a denominator to divide by. Only 1 quadrat of 22 has a background moving more than 1.5x a roof. At 70-150 DN of MAD against 2,000-2,800 DN of reflectance, what is being measured is a common floor of per-scene radiometric residual and BRDF, not surface dynamics, and a floor is added AFTER mixing so it is not attenuated by f at all.

The ablation agrees, over 30 quadrats leave-one-quadrat-out: +0.0003 AUC (17 of 30 folds, p=0.46) and +0.0011 within size band (18 of 30, p=0.26). Damping plus size alone reaches 0.7388 / 0.5863, against a size-only baseline of 0.7441 / 0.5773.

The conditional test is what makes this more than another null. Registered before the run: if the mechanism is real but starved, the gain should rise with how much a quadrat's background actually moves. It does. Spearman between the within-size gain and open_over_roof is +0.401 (p = 0.071, n = 21 folds), and splitting at the median:

	Gain within size band	Folds better
Dynamic half (open/roof >= 1.13)	+0.0015	9 of 11
Still half (open/roof < 1.13)	-0.0001	4 of 10

So the physics is visible in the structure of the result and worth nothing in its magnitude. Treat that cautiously: n is 21, p = 0.071, and the single most dynamic quadrat (malok, open/roof 2.02) runs against the trend at -0.0013, so the relationship is not driven by its own extreme.

The most useful thing here is why the denominator is missing, because it is self-inflicted. annual_composite takes a DRY-SEASON window of the twelve least-cloudy scenes, chosen precisely to suppress phenological and atmospheric variation and produce a stable composite. That is the right choice for everything else the pipeline does, and it removes exactly the background dynamics this estimator needs. The honest next test is a full-year stack including the monsoon, where cropland actually swings; that is different data, not a different estimator, and it is filed in Open questions. Until then this is rejected for the window the project actually composites.

A methodological note worth keeping: the first precondition check measured temporal amplitude over WHOLE quadrats and looked ample (median 148 DN, all 31 quadrats clearing the bar). The denominator that matters is the background near a BUILDING, which is other roofs and pavement, and measuring that population gave 1.12. Checking the precondition on the wrong population is the same error the snow-cover experiment records, in a different costume.

Artifacts: results/pv_temporal_invariance.json, results/roofclf_temporal_unmix.json, results/roofclf_temporal_unmix_folds.csv.

The model class: ranking and calibration disagree (2026-09-19)

Every feature block this project has measured -- SPPI, glint, the yard, epoch jump, temporal statistics, temporal unmixing, sharpening, spatial unmixing, L1C -- was priced against fit_logistic, an L2-regularised LINEAR model. A linear score cannot express a conjunction, and "is this a PV roof?" reads like one: dark AND spectrally flat across the visible AND the right SWIR drop AND large AND in a dense-adoption area. Six blocks landing within 0.005 of each other is consistent with the functional form being the constraint rather than the features, so it is worth testing directly.

Two declared HistGradientBoostingClassifier configurations, conservative and larger, fixed in the script rather than selected on the held-out folds, on the same 30 quadrats and the same table.

Model and features	AUC	Within size band	Median per-quadrat rate error
Logistic (shipped)	0.8574	0.8206	0.0308
Logistic + shape	0.8593	0.8196	0.0309
GBM small	0.8582	0.8006	0.0204
GBM large	0.8526	0.7979	0.0177
GBM large + shape	0.8618	0.7921	0.0165

On ranking the linear model wins, and not marginally. Paired per fold, every GBM variant loses: -0.0089 to -0.0121 AUC and -0.0163 to -0.0231 within size band, better in only 4 to 6 of 30 folds, p <= 0.002. The larger configuration is worse than the smaller one, which is the signature of a flexible model learning each quadrat's idiosyncrasy rather than what transfers between them. Note that gbm_large_shape has the highest median AUC in the table (0.8618) while losing on 25 of 30 folds: a difference of medians is not a paired gain, which is the same trap the shape block below fell into.

So the linear model is not the bottleneck, and the feature experiments were fair. That is worth having established, because it is the assumption every one of them rested on.

On calibration the answer reverses, and for this project that matters. The atlas consumes an aggregate adoption rate, not a ranking, and the register already records that ranking transfers across quadrats while absolute rates do not -- which is why the coverage-ratio-by-size-and-density machinery exists at all. Measured per quadrat, GBM predicts the adoption rate substantially better: median absolute error 0.0308 to 0.0165, closer to truth in 20 of 29 quadrats for gbm_large (Wilcoxon p = 0.006) and 21 of 29 with shape (sign test p = 0.024).

The obvious objection is that a model can tighten rate_ratio by hedging toward the global mean, which would be worthless. It is not doing that: the spread of predicted rates across quadrats is 0.0806 against a true 0.0798, and the correlation with the true rate RISES from 0.776 to 0.869. It keeps the dispersion and gets the level right more often.

The two objectives genuinely disagree, and nobody had noticed because only AUC was reported. Nothing is being swapped on the strength of one run: the lead product and the precision-thresholded population that feeds the coverage ratio both depend on per-building ranking, where the linear model is better, while the capacity half depends on the rate, where it is not. The honest next step is to price a GBM end to end through sub400-capacity against the same quadrats, not to change the classifier.

Footprint shape, settled

plus_shape had scored 0.8593 against the shipped 0.8574 in the ablation table and had sat unadopted since 2026-08-09, the best-looking block never promoted. Given the paired test every rejected idea got, it is noise: -0.0001 AUC, 14 of 30 folds, p = 1.00, and -0.0003 within size band. The apparent gain was a difference of medians across folds rather than a per-fold improvement. Closed.

Artifacts: results/roofclf_model_class.json, results/roofclf_model_class_folds.csv.

Aiming at the aggregate: weighting and recalibration (2026-09-19)

The model-class test found that gradient boosting ranks worse and predicts per-quadrat adoption rates better. Two cheap interventions follow from it, neither touching the feature set, both aimed at the aggregate the atlas consumes rather than at AUC.

Variant	AUC	Within size band	Median per-quadrat rate error	Area ratio
Baseline (shipped)	0.8574	0.8206	0.0308	1.084
Area-weighted loss	0.8506	0.8046	0.0395	1.035
Isotonic, global	0.8570	0.8199	0.0268	1.095
Isotonic, by size tercile	0.8560	0.8166	0.0253	1.128
Platt	0.8574	0.8206	0.0287	1.070
Gradient boosting, for reference	0.8526	0.7979	0.0165	--

Area weighting does what it promises, and that is not what the product needs. Capacity is area-weighted while the fit treats a 30 m² shed and a 390 m² warehouse as equally important rows, so weighting the likelihood by roof area is the obvious correction. It improves the quantity it optimises -- the predicted-over-true flagged roof AREA moves 1.084 to 1.035 -- and it is worse at everything else: -0.0111 AUC, -0.0160 within size band, and a COUNT rate error that rises 0.0308 to 0.0395. Trading the population that carries the labels against the aggregate loses more than it gains at this sample size. Available as fit_logistic(sample_weight=...), off by default, and None reproduces the unweighted fit bit-for-bit.

The calibration gain is not recoverable by recalibration, which is the useful half. A monotone map cannot change a ranking, so if gradient boosting were merely a better-calibrated version of the same score, isotonic or Platt would capture it for free. They do not. The best variant, isotonic fitted within roof-area terciles, cuts the median per-quadrat rate error 0.0308 to 0.0253 and is closer in 19 of 29 quadrats, but at Wilcoxon p = 0.069 it is not significant, and it recovers only 38% of the gradient-boosted improvement (which reached 0.0165 at p = 0.006). Platt moved AUC by exactly 0.0000 in 0 of 30 folds, which is the clean check that a strictly monotone map reorders nothing; isotonic moves it by 0.0002 only because it creates ties.

So gradient boosting's advantage is a genuine reordering of buildings, not a calibration curve, and it cannot be had cheaply. That sharpens open question 20: if the aggregate matters more than the ranking for the capacity half, the GBM has to be deployed properly, plumbing and all, rather than approximated by post-processing the linear model.

Calibrators were fitted NESTED -- inside each leave-one-quadrat-out fold the 29 training quadrats were split into five blocks by quadrat, and the calibrator was fitted on the inner out-of-fold scores. Fitting on in-sample scores would be optimistic in exactly the direction being measured. Artifacts: results/roofclf_weighting_calibration.json, results/roofclf_weighting_calibration_folds.csv.

Spectral coherence, and where transferability lives (2026-09-19)

Two more attempts at the features the classifier leans hardest on, both using data already on disk.

Variant	AUC	Within size band	Median rate error	Paired vs shipped
Baseline (shipped)	0.8574	0.8206	0.0308	--
Medoid composite	0.8415	0.7896	0.0280	-0.0074, 9 of 30
Plus context features	0.8540	0.8166	0.0291	-0.0003, 14 of 30
Context features ONLY	0.8595	0.8189	0.0569	-0.0011, 14 of 30
Medoid plus context	0.8546	0.7982	0.0287	-0.0070, 9 of 30

The band-wise median really does invent a spectrum, and fixing it does not pay. annual_composite reduces with median(dim="time") over a Dataset, so each band's median is taken independently: a pixel's B02 can come from January and its B11 from March, and the result is a spectrum no scene observed. That is harmless for one band and not harmless for a RATIO, which is what the model weights most (swir_vis_ratio -3.92, ndbi +3.65, blue_red_ratio -1.56). A medoid -- the single date per pixel whose whole spectrum is closest to the median -- is coherent by construction, and verified to equal exactly one real date where the band-wise median equals none. It scores -0.0074 AUC and -0.0102 within size band. The trade is visible in the construction: a medoid is ONE observation, so it forfeits the twelve-scene median's noise averaging, and that costs more than incoherent ratios do. It does improve the rate error slightly (0.0308 to 0.0280), which is consistent with the diagnosis rather than with the medoid being simply worse.

The result worth keeping is the context-only row. local_zscore re-centres a building against its own quadrat or cell, and ships applied to brightness alone, one feature of fifteen. Extended to every spectral feature, added alongside the absolute ones it changes nothing (-0.0003, 14 of 30). But replacing them entirely ties the shipped model (-0.0011, 14 of 30, p = 1.00) while nearly doubling the rate error, 0.0308 to 0.0569.

That separates two things this project had confounded:

• Ranking is relative. Which building in a cell carries PV is fully answerable from within-cell contrast; the absolute reflectance level adds nothing to it.
• Calibration is absolute. Z-scoring removes the level that says one quadrat is more PV-dense than another, and the aggregate collapses.

For scaling that is a useful thing to know in both directions. The ranking half -- the leads product, and the precision-thresholded population the coverage ratio is fitted on -- should transfer to a new country robustly, because it is immune to the cross-quadrat level shifts that atmospheric and seasonal differences produce. The aggregate half depends on absolute reflectance being comparable between places, which is exactly why removing atmospheric correction in the L1C test tripled the fold-to-fold spread without touching the median.

A footnote that turned out to be a defect. brightness_zscore re-centres a building's brightness against its own quadrat or cell and has shipped as an available block, plus_local_contrast, since 2026-08-09. It had never actually been measured: local_zscore used np.median rather than np.nanmedian, and it runs inside building_table BEFORE the fill/edge rows are dropped, so a single building whose footprint has no valid pixel turned the entire quadrat's z-scores into NaN. That was 21% of rows and 5 of 30 quadrats entirely NaN -- among them sialkot and sukkur, the two quadrats already recorded here as carrying composite fill. One NaN poisons a logistic fit, so every fold trained on those quadrats returned NaN and pivot_table silently dropped the block from the ablation output. Fixed 2026-09-19 (NaN-safe, and bit-identical on clean input), after which the block measures +0.0000 AUC, 13 of 30 folds and is rejected on its merits rather than by accident.

Neither is adopted. The context block is kept as roofclf.CONTEXT_FEATURES / add_context_features, the medoid as preprocess.medoid_composite and building_table(preprocess="medoid"). One caveat on the medoid comparison: its table has 36 more rows than the baseline (123,903 against 123,867), because a scene stack covers the quadrat geobox plus margin rather than the boundary bbox. At 0.03% that cannot carry the result, but the two are not row-identical the way the earlier preprocessing comparisons were.

Artifacts: results/roofclf_medoid_context.json, results/roofclf_medoid_context_*_folds.csv.

The spectral SNR budget, and why the domain is exhausted (2026-09-19)

After a run of null preprocessing results it is worth asking what the spectral signal actually IS, rather than trying another representation of it. Detection here is a mixture, y = f.r_PV + (1 - f).r_bg, so the signal is f.(r_PV - r_bg) and the question is what each term is worth. Measured on the 30-quadrat table, in B08, the highest-contrast band:

Term	Value
Panel-minus-roof contrast at full cover	0.0579 reflectance = 1.34 background sd
Median fill fraction on a PV roof	0.309
Typical available signal	0.41 sd
Background sd between PV-free roofs	0.0431 reflectance
Sentinel-2 radiometric noise at this level	~0.001-0.002 reflectance

The mixture model holds tightly: B08 falls linearly with fill fraction (B08 = 0.3142 - 0.0579 x f), and the separation grows with it exactly as predicted -- d' of -0.25, -0.36, -0.54, -0.83 across fill-fraction bands averaging 0.08, 0.15, 0.29 and 0.65.

The noise is not the sensor, and this rules out a whole class of ideas by arithmetic. Roof-to-roof heterogeneity is twenty to forty times Sentinel-2's radiometric noise. Every intervention aimed at measurement quality -- more scenes, a better atmospheric correction, denoising, spectral coherence -- is tuning a term worth about 2% of the variance. That is the retrospective explanation for temporal statistics, L1C, the medoid and sharpening all landing at zero.

The linear spectral limit is already reached. The Mahalanobis separation across all ten bands is d' = 1.226, an implied AUC of 0.807, while spectral_only measures 0.8333 in leave-one-quadrat-out -- the shipped model is at or past the Gaussian linear bound, exceeding it because the hand-made ratios are nonlinear in the raw bands. Whitening buys 1.95x over the best single band (0.629, B08) and the model already has it. With 79.3% of band variance in the first principal component and a maximum inter-band correlation of 0.984, there are roughly two effective dimensions here. No new index, matched filter or whitening of these bands can add meaningful signal. (The bound is in-sample and assumes Gaussian classes, so read it as an order of magnitude, not a decimal.)

That leaves shrinking the denominator, which fails for an interesting reason. Since the noise IS roof diversity, estimating a background per building from its nearest neighbours should raise SNR directly, and roofing material is spatially clustered so the neighbours are the right reference. It does cut the noise: the PV-free standard deviation falls 23 to 43% across bands. It cuts the signal faster. Median d' falls to 0.36x, B11 even changes sign, and the multivariate separation drops 1.226 to 0.904.

The cause is a property this project already documents: PV adoption is spatially clustered, which is why nn_median_m exists. A PV roof's neighbours disproportionately carry PV, so the local median is itself pulled toward the panel spectrum and subtracting it cancels the contrast. Sweeping the neighbourhood scale shows there is no escape:

Neighbours	Median radius	Mahalanobis d'	Against raw
10	31 m	0.717	0.58x
50	78 m	0.904	0.74x
200	169 m	0.980	0.80x
800	369 m	1.022	0.83x
none	--	1.226	1.00x

Monotone, and asymptotic toward not conditioning at all. This also explains why the quadrat-scale version (context features) merely TIED rather than helped or hurt: at 1-4 km² the group is large enough that adoption clustering does not dominate its median, so it neither cancels signal nor removes much heterogeneity.

Conclusion: the spectral domain is exhausted for this sensor. The remaining term in the budget is fill fraction, which no amount of processing changes -- it is set by pixel size against array size, and it is the same quantity that makes France fail and Pakistan work. Kept as roofclf.add_neighbour_features for anyone who wants to re-measure. Panels are strongly polarising, which would be a nearly background-free channel, but no free satellite measures it.

Sun geometry: neither glint nor high sun raises the contrast (2026-09-19)

Two follow-ups to the SNR budget, asking whether some sun-sensor geometry gives a better look at the same panels.

Glint is not a scene-level lever. A glass-fronted panel is specular, so on a date when the sun-panel-sensor geometry approaches the mirror condition it should stand out. Scored over 239 scenes in 22 quadrats, using each scene's own sun and view angles and the 192 poses fitted from real Pakistani installations rather than an assumed one, near-specular scenes do show 34% more contrast: d' -0.549 in the nearest quartile of misalignment (1.17 deg) against -0.409 in the farthest (8.54 deg).

It is not glint. Misalignment and solar zenith are collinear at +0.475 by construction, and partialling out the sun kills the geometry term while the sun survives:

	Spearman	p
d' vs misalignment	+0.101	0.12
d' vs solar zenith	+0.204	0.0015
d' vs misalignment, controlling for solar zenith	+0.025	0.70
d' vs solar zenith, controlling for misalignment	+0.161	0.013

The sign was wrong for glint from the start: PV gets DARKER relative to the roof near specular, not brighter. And glint could not work as a scene selector even if it were real, because 93.7% of scenes already have some pose within 10 deg of specular (median 3.77 deg) -- the condition almost never discriminates between observations. What this does NOT test is per-target glint on individual installations, which is how glint.py uses it and where it does work: no scene has more than half the pose population glinting, so a narrow lobe on a few panels is diluted by pixel-averaging across a quadrat.

And a high-sun window does not help either, which refutes a prediction made here. The winter fit was d' = -1.296 + 0.0145 x sun_zenith, which extrapolated to roughly double the contrast at summer sun angles and suggested the dry-season compose window might be costing signal. Measured against pre-monsoon May-June scenes over 10 quadrats:

	Pre-monsoon	Dry season
Median d'	-0.601	-0.672
Roof brightness	3,064 DN	2,532 DN

Gain 0.98x, better in 5 of 10 quadrats, Wilcoxon p = 0.77. The first half of the mechanism happened exactly as predicted -- roofs really are 21% brighter under high sun -- and the contrast did not move, because d' is a NORMALISED quantity: illumination scales the panel, the roof and the between-roof spread together, so numerator and denominator grow in step and the ratio is invariant to first order.

That means the within-window correlation the prediction rested on was not illumination scaling at all; more likely seasonal surface change (moisture, dust, vegetation) tracking date across a narrow 37-59 deg span. The compose window is not costing contrast, and acting on the extrapolation would have meant recompositing a country onto a worse epoch for no gain. Artifacts: results/glint_geometry_snr.json, results/summer_window_contrast.json and their per-scene CSVs.

The level half of brightness does not transfer (2026-09-20)

absolute = cell_mean + deviation, and roofclf sees only absolute, which confounds "this building is bright" with "this cell is bright". Handing it the cell mean adds one degree of freedom per feature. The motivation was the measured split reported above: within-cell z-scores alone tie the shipped ranking while doubling the rate error, so ranking is relative and calibration is absolute. The level half looked like the unexploited part.

Registered before measuring: a cell-constant feature adds the same number to every building's logit inside a held-out quadrat, so it cannot reorder them, and any AUC movement can only come from refitting the other coefficients. That half held -- the single cell-mean moved AUC by +0.0001.

The substantive half failed, and significantly.

Block	AUC	Within size band	Rate error	Rate ratio
Baseline	0.8574	0.8206	0.0308	1.061
Plus brightness cell-mean	0.8571	0.8220	0.0309	1.056
Plus all 15 cell-means	0.8494	0.8229	0.0430	1.012

Calibration gets worse: the per-quadrat adoption rate is closer to truth in only 7 of 29 quadrats, sign test p = 0.008, Wilcoxon p = 0.002. The 15-feature version pushes median rate error 0.0308 to 0.0430 while pulling the ratio to 1.012, which is the signature of centring the level and widening its spread.

The reason is this register's own finding, turned against the idea. A cell-constant feature is fitted across 29 quadrats and then extrapolated to an unseen one, so the model learns a BETWEEN-quadrat relationship between brightness and adoption rate -- and between-quadrat absolute rates are exactly what does not transfer. Adding the level as a feature does not give the model the level; it gives it a spurious slope to extrapolate along. Kept as roofclf.CELL_LEVEL_FEATURES / add_cell_level_features for re-measurement.

Snow in Germany: the opportunity is real, the contrast is not concludable (2026-09-20)

Snow-cover contrast was rejected for France on opportunity, 0.096 usable scenes per commune-winter outside the Alps, and that entry named the one variant worth testing: Germany, which has more reliable lowland snow. Tested over the 8 densest PV cells in the German OSM rooftop layer, three winters, with SCL class 11 deliberately KEPT (the shipped mask drops classes outside 4-7 and would discard every observation under test) and snow measured over building footprints rather than over the cell.

The opportunity objection is answered. 6 snow scenes out of 58 low-cloud scenes, a 10.3% rate, against France's 0.096 per commune-winter. Observations of snow-covered roofs genuinely exist in Germany.

The contrast is not. Six scenes, Mann-Whitney p = 0.39, and bimodal:

Cell	Snow on roofs	Roof DN	PV DN	d'
137_515	61%	2,035	1,146	-2.40
137_515	100%	3,806	2,405	-1.72
149_513	20%	1,647	829	-1.03
149_513	100%	4,333	1,702	-1.74
60_508	100%	3,250	8,143	+3.84
60_508	84%	3,524	5,588	+2.48

Four scenes behave as the physics predicts. The two that inverted are explained by array size rather than by snow: cell 60_508's mapped arrays have a median area of 126 m², about 1.3 pixels at 10 m, against 338 and 320 m² in the two well-behaved cells. At roughly 1.2 PV pixels per array the mask is mostly snow-covered ROOF, which reads brighter than a roof average that includes shadowed and wet surfaces. The project's central limitation, array size against pixel size, reappears here amplified rather than relieved.

The headline ratio of 15.9x is not usable either: it divides by a bare-season baseline of -0.087, i.e. German OSM rooftop PV barely separates from PV-free roofs in bare winter at all, which is consistent with a control contaminated by the 96.4% of German rooftop PV that OSM does not map. A near-zero denominator makes any ratio large.

What would settle it is more cells and a restriction to arrays large enough to own a pixel. Recorded as partial rather than shipped or rejected, because the half that killed the French version is genuinely answered and the half that matters is untested at this sample size. Artifacts: results/germany_snow_contrast.json, results/germany_snow_contrast_scenes.csv.

The composite reducer is the biggest lever in this register (2026-09-20)

Chasing Google's Open Buildings 2.5D Temporal, which fuses up to 32 Sentinel-2 acquisitions to reach an effective ~4 m, produced the largest single improvement measured anywhere in this register -- and it turned out to have nothing to do with super-resolution.

annual_composite reduces the scene stack with a per-pixel median. Replacing it with a mean gains +0.0188 AUC within size band at 10 m, with no change to resolution, features or model.

That figure is smaller than first reported here, and the correction matters. The variants below reduce the saved SCENE STACKS while the baseline reduces the COMPOSITE FILES, and the two were fetched at different times through different read paths. A control run afterwards -- the MEDIAN of the same stack, which should score zero if the two paths are equivalent -- scores +0.0098 within size band (21 of 30, p = 0.024). So roughly a third of the original +0.0286 was the path, not the estimator:

Effect	AUC	Within size band
Path (stack median against composite median)	+0.0054	+0.0098
Estimator (mean against median, same stack)	+0.0122	+0.0188
Estimator, trimmed mean	--	+0.0102

The estimator effect is real and significant either way, and the path effect is worth its own look: the baseline reads through composite_index.read_window, which mosaics and can carry the documented cell-edge fill frame, while a stack is a clean geobox read. If that is the cause it is a free win independent of any reducer.

The decomposition that isolates it:

Variant	AUC	Within size band	Folds better
Baseline, median at 10 m (composite file)	0.8575	0.8206	--
Median at 10 m (same stack) -- PATH CONTROL	0.8628	+0.0098	21 of 30
Mean at 10 m	0.8756	+0.0286 (+0.0188 net of path)	25 of 30
Mean at 5 m	0.8789	+0.0313	25 of 30
Multi-frame fusion at 5 m	0.8767	+0.0273	26 of 30
Area-weighted zonal means at 10 m	0.8588	+0.0113	25 of 30
Area-weighted zonal means, as SHIPPED (nodata-masked, no rows dropped)	0.8682	+0.0119	24 of 29
Median at 5 m, nearest	0.8623	+0.0058	23 of 30
SEN2SR single-image at 2.5 m	0.8402	-0.0367	6 of 30

So 91% of the effect is the estimator, about 9% is the finer grid, and the fusion itself is NEGATIVE: applying measured sub-pixel shifts scores below not applying them.

Why the mean wins is not mysterious once separated out. SCL masking already removes cloud, shadow and snow, so the median's robustness is largely redundant, and at twelve samples a median carries about 1.57x the variance of a mean. Less feature noise, better separation, and it helps most for the sub-pixel footprints whose single pixel is noisiest.

The route there was three wrong mechanisms in a row, which is worth recording. The gain first looked like multi-frame fusion; a zero-shift control beat the fused version, so it looked like resolution; plain upsampling recovered only a fifth of it, so it looked like footprint sampling; exact area-weighted zonal means recovered a third. Only the mean explains it, and tmean_up2 reproduces the shift-and-add control to four decimals, which is the consistency check that the accumulator had been computing a mean all along.

Why fusion cannot work here, measured rather than assumed. Multi-frame super-resolution needs frames that sample the ground at different sub-pixel phases. Measured across 298 frame pairs in 30 quadrats, the median inter-acquisition shift is 0.122 px (1.22 m), with phase offsets at roughly a quarter of the uniform-spread ideal and only about 3 frames in 12 usefully displaced. Re-measured from native granules with no reprojection or resampling, to rule out our own loading path as the cause: 0.132 px (1.32 m), essentially identical. The diversity really is that small. A synthetic check puts the consequence precisely: with ideal phases shift-and-add recovers +0.166 of correlation against an upsample, and at the diversity Sentinel-2 actually provides, +0.015 -- about 9% of the achievable gain.

And single-image super-resolution actively hurts. SEN2SRLite (CC0-1.0, runs in 0.31 GB on a GTX 1060) produces a faithful 2.5 m raster -- downsampled it correlates 0.9976 with the original and it genuinely adds high-frequency energy, 0.688 to 0.918. It still costs -0.0367 AUC within size band, 6 of 30 folds, because it cannot add information: it redistributes 10 m content using learned priors, and texture uncorrelated with PV dilutes real contrast. That is the 2026-07 rejection reproduced with a much better model, and it answers the hallucination objection empirically rather than by assertion.

Building height, from GlobalBuildingAtlas, is redundant. Height alone separates PV from PV-free roofs at d' = +0.651 (median 6.74 m against 4.03 m), comparable to the best single spectral band. On top of the model it is worth +0.0006 within size band, 6 of 9 folds, p = 0.51, and height-plus-size alone scores 0.173 BELOW baseline: tall buildings are large buildings, and log_roof_area already carries it. Measured on the 5 quadrats where GBA covers more than half our footprints, a biased subsample. GBA heights are CC BY-NC 4.0 in any case, which would have blocked deployment.

The mean's weakness is real, and a trimmed mean is the answer. The median is there to reject residual cloud that SCL missed, and a mean cannot: with one unmasked cloud pixel among twelve frames, a clean value near 100 reads 100.7 under the median, 100.2 under a 20%-trimmed mean, and 924.4 under a plain mean. Measured on the quadrats, the trimmed mean keeps most of the gain:

Reducer	Within size band	Folds better	One unmasked cloud
Median (current)	--	--	100.7
Trimmed mean (drop top and bottom decile)	+0.0200 (+0.0102 net of path)	25 of 30	100.2
Mean	+0.0286 (+0.0188 net of path)	25 of 30	924.4

So the trimmed mean is the one to ship: 70% of the gain, and robustness indistinguishable from the median on the failure mode the median exists for.

Wired as imagery.REDUCERS / annual_composite(reducer=...) / compose --reducer, with the default left at "median" and run_compose inheriting an AOI's existing reducer, exactly as for the resampling change and for the same reason: a model calibrated on medians and scored on means is a domain shift. Every existing AOI -- Pakistan, Germany, France, Nigeria, Zambia -- inherits "median". Shipping it nationally means recompositing, which open question 18 prices at roughly 2 TB of transfer per country, so it is free for the next country and expensive for the existing ones.

Two more noise reductions, and they stack. Weighting each 10 m pixel by the fraction of the footprint covering it -- instead of rasterize's all-or-nothing pixel-centre test, which gives about half of VIDA footprints zero pixels and a representative-point fallback -- is worth +0.0113 within size band on its own (25 of 30 folds, p = 0.0001). Combined with the trimmed mean it reaches +0.0305 raw, +0.0207 net of the path effect, against +0.0215 predicted by adding them: additive to within noise, because they attack different noise sources.

SHIPPED 2026-09-20, and it is the only part of this section that costs nothing. The reducer and the frame count both need a country recomposited; area weighting is a read-side change to roofclf and needs no new imagery at all, so it was promoted to the default (roofclf.AREA_WEIGHTED_ZONAL, roofclf.area_weighted_zonal_mean, applied in BOTH building_table and score_buildings_national, with --no-area-weighted on roof-classifier and roofclf-score-national reproducing every earlier figure).

The shipped version is not the experimental code, so it was re-measured rather than carried over. Two things had to change first, neither of which shows up in an interior quadrat:

• The nodata mask. The experiment tested np.isfinite, right for a NaN-masked scene stack and wrong for a composite window, where fill is 0.0. Unmasked, a cell-edge fill pixel enters the weighted mean as genuine near-zero reflectance, and PV is dark: that is the mechanism that once made 45.6% of every nationally flagged building a cell-edge artefact. The shipped version applies the same all-bands-equal-fill test zonal_mean_max uses.
• The fallback. A footprint with no subpixel of its own got zero weight and came back NaN, silently dropping 0.16 to 0.47% of buildings per quadrat -- the smallest ones, i.e. the population this module exists for. The shipped version keeps zonal_mean_max's value for those rows, so the scored population is unchanged by construction. 0.06 to 0.8% of buildings take that path.

Re-measured on all 30 quadrats, both arms on identical rows, no rows lost:

Metric	Pixel-centre	Area-weighted	Delta	Folds better	Sign p	Wilcoxon p
AUC	0.8591	0.8682	+0.0085	28 of 29	0.0000	0.0000
Within size band	0.8093	0.8193	+0.0119	24 of 29	0.0005	0.0017

and in the units the atlas consumes, at an identical precision of 0.500:

Arm	Flagged	Precision	Recall
Pixel-centre	21,904	0.500	0.6386
Area-weighted	22,580	0.500	0.6583

+2.0 points of recall, 3.1% relative, for no imagery. The shipped version slightly exceeds the experimental +0.0113, which is the expected direction: it stopped dropping the smallest buildings. Acceptance test: scripts/verify_area_weighted_default.py, result results/roofclf_area_weighted_default.json.

Pakistan was refitted on 2026-09-20 (30 quadrats, parcel label; pre-refit state kept at data/roofclf_PRE_areaweighted_20260920/). Against the 2026-08-20 production fit:

	2026-08-20	2026-09-20
Buildings	123,867	123,898
Median fold AUC	0.8574	0.8682
Median within size band	0.8206	0.8193
Deployment threshold	0.2515	0.2446
Flagged at precision 0.500	21,580	22,580
Recall at precision 0.500	0.6292	0.6583

The within-size line reads down and that is a difference of medians, not a regression. Two things changed between those summaries -- area weighting and the grid-snap read fix -- and neither summary is a paired comparison: different tables, different fold sets, and a median of fold medians is not additive. Measured properly on identical rows the two are +0.0119 (24 of 29 folds, p = 0.0017) and +0.0032 (18 of 29, p = 0.27) respectively; the read-path figure reproduces the +0.0032 already recorded for it, and its own unpaired medians move 0.8206 to 0.8092 while its paired delta is positive. This register has retracted a claim to this exact trap once already ("the 0.8593 that made it look best was a difference of medians"), so the paired test is the one to read. The deployment row is computed on pooled out-of-fold scores rather than a median, which is why it moves unambiguously.

The calibration and the national scoring must share one zonal convention, and check_scoring_matches_calibration now refuses a mismatched pair by name rather than by coefficient hash. National rescoring was run the same day off this fit; the capacity chain and the atlas are a separate, owner-gated step because they move the published headline.

Frame count helps, weakly, and only through variance. Full-year stacks (~36 scenes against the dry-season 12) separate frame count from season by drawing a fixed random 12 out of the SAME year stack, so the two differ only in N:

Trimmed mean over	Within size band	Net of path
12 dry-season frames	+0.0200	+0.0102
12 frames of the year	+0.0247	+0.0149
~36 frames of the year	+0.0295	+0.0197

The frame-count effect is +0.0048 within size band for tripling N, and the path effect cancels in that comparison so it is clean. That is what 1/sqrt(N) predicts: three times the frames cuts the standard error by 42%. So Google's 32-acquisition budget does transfer to this problem -- through estimator variance, not through the shifted viewpoints its design exploits -- and it costs three times the download, which against open question 18's roughly 2 TB per country is a poor trade at national scale. Note also that the full-year season mix is not itself harmful: at matched N = 12 the year frames slightly outscore the dry-season ones, so a wider window is a viable way to reach more frames if the bandwidth is ever there.

The whole package, net of the path effect: about +0.026 AUC within size band, from a trimmed mean, area-weighted zonal means and more frames -- all of it noise reduction, none of it new information, and none of it super-resolution.

What it is worth to the PRODUCT, which is not AUC. run_roof_classifier picks a threshold targeting precision 0.5 on pooled out-of-fold scores, and sub400_capacity fits its coverage ratio and area recall on the population that threshold flags. A ranking gain that leaves that population unchanged changes no capacity figure, so the deployment step was re-run for each variant:

Variant	AUC	Flagged	Precision	Recall
Baseline	0.8601	21,592	0.500	0.6295
Trimmed mean	0.8771	23,148	0.500	0.6751
Plus area-weighted zonal	0.8810	23,382	0.500	0.6822
Plus the full year's frames	0.8851	23,794	0.500	0.6937

At identical precision, recall goes 0.6295 to 0.6937: +6.4 points, or 10.2% more of the true PV roofs captured. That is the number to quote, because it is the population the capacity chain is fitted on. The two free changes alone -- trimmed mean and area-weighted zonal -- deliver +5.3 points of it; the remaining +1.1 costs three times the download.

One threshold is already the right one (2026-09-20)

If the deployment threshold is what the capacity chain consumes, the obvious next lever is the threshold itself. run_roof_classifier picks ONE cut targeting precision 0.5 on pooled out-of-fold scores. One cut is recall-optimal only if the score means the same thing everywhere, and this register has repeatedly found it does not: rate_ratio spans 0.2 to 5x across quadrats, gradient boosting beat the linear model on per-quadrat rate error while ranking worse, and no global monotone map recovered that gain. So: cut per stratum instead, on the two stratifiers the capacity chain already uses, building-size band and quadrat density. Every threshold is fitted on the OTHER quadrats' out-of-fold scores, the same nesting the model itself uses, and every comparison is against a global cut RE-TUNED to whatever precision the stratified rule actually reached, so a recall gain bought by dropping precision cannot read as a gain.

Equal precision in every stratum is much worse than one cut, and not marginally:

Rule	Flagged	Precision	Recall	Net of matched global	Quadrats better	Sign p
One global cut	21,199	0.5012	0.6195	--	--	--
Per size band	20,180	0.5004	0.5889	-0.0298	7 of 26	0.029
Per density stratum	19,074	0.5023	0.5586	-0.0579	3 of 23	0.0005
Per size and density	19,029	0.4885	0.5420	-0.0919	4 of 25	0.0009

The mechanism is that equal AVERAGE precision is the wrong objective. At a fixed pooled precision the recall-maximising allocation equalises the MARGINAL precision -- the precision of the last building admitted -- not the stratum's average. A stratum whose base rate is 3.6% (the 0-50 m² band, 50,502 of 123,867 buildings) has to be cut very deep before its average precision reaches 0.5, and its recall collapses from 0.191 to 0.059 while the bands that were already easy barely move.

So the theoretically-correct version was run too: recalibrate the score to a per-stratum posterior with isotonic regression fitted on the training folds, then apply ONE global cut to the recalibrated score, which equalises the margin by construction. This is not the monotone recalibration already rejected here -- that map was global, and a per-stratum map is not monotone in the original score.

It wins pooled and dies paired. Per size band: recall 0.6392 at precision 0.4947 against the matched global cut's 0.6260, so +1.3 points pooled -- and a median per-quadrat difference of exactly +0.0000, 12 of 24 quadrats better, sign test p = 1.00. The pooled gain is a few large quadrats, which is the same failure the register logged when 55 PV-dense German municipalities read as an 18.2% error against a representative 33.4%. Recalibrating per density stratum loses outright (-0.0482 net), and per size and density together loses (-0.0110 net).

The roof is its own control (2026-09-20)

Everything above works on ONE epoch, and the SNR budget says that is the binding constraint: the noise limiting roofclf is roof-to-roof heterogeneity, 0.0431 reflectance, 20 to 40x the sensor noise, and the linear spectral limit on a single-epoch zonal mean is essentially reached. Every intervention aimed at the MEASUREMENT -- L1C, 20 m sharpening, spatial unmixing, single-image and multi-frame super-resolution, medoid compositing -- has failed, which is exactly what that budget predicts.

There is one term that cancels roof-to-roof heterogeneity outright, and it is not spectral: the same roof, earlier. Pakistan's rooftop boom is post-2022, so a 2019/20 dry-season composite is a pre-installation look at most of today's arrays, and current - 2019 differences away everything about the roof that did not change. Thirty-one quadrat-clipped scene stacks were pulled for 2019-11-01 to 2020-03-15 (6 to 12 frames, median 7, 223 MB total) and reduced the SAME way as the current stack, so a difference cannot pick up a reducer or a read-path change.

Block	AUC	Within size band	vs baseline	Folds better	Sign p	Wilcoxon p
Baseline	0.8574	0.8205	--	--	--	--
Plus the 2019 difference	0.8632	0.8388	+0.0234	25 of 29	0.0001	0.0002
Plus a split-half difference (placebo)	0.8551	0.8182	-0.0009	14 of 29	1.00	0.70
Plus the 2019 LEVELS, undifferenced	0.8590	0.8294	+0.0036	20 of 29	0.061	0.26
Plus both differences	0.8695	0.8392	+0.0222	25 of 29	0.0001	0.008

Measured against the shipped read path this is the largest single feature block in this register, and roughly what the entire noise-reduction package above is worth, from one extra historical composite rather than a national recomposite. That framing does not survive its own control -- see "The two levers are not additive" below, which is the part to read before acting on any of this.

Three controls, all registered before measuring, and all of them matter:

The placebo is exactly null. Differencing the first half of the CURRENT dry-season stack against its second half gives the same band count, the same features, the same imagery and the same reduction, with no installation possible in between: -0.0009, 14 of 29 folds, p = 1.00. So what helps is the EPOCH, not having two estimates of the same roof.

The difference beats the levels, which is the L2 prior doing work. For an unpenalised linear model, adding the 2019 levels spans the same space as adding the differences and must tie. Under L2 it does not, and it does not: +0.0036 (p = 0.061) against +0.0234. The difference parameterisation is the one that encodes "the roof cancels", and encoding it is worth 6x more than handing the model the same information unshaped.

It is PV, not construction. A building that did not exist in 2019, or was re-roofed, also differences large, and VIDA footprints come from recent imagery so new construction is in the layer. Two reads separate them (scripts/epoch_difference_mechanism.py):

• Sign. PV modules are dark; bare ground turning into a roof is not. PV-bearing roofs darkened relative to PV-free ones by a median 0.0082 reflectance, darker in 28 of 29 quadrats, sign test p < 0.0001.
• Magnitude. Construction would live in the extreme tail of the difference. It does not: the top decile of |d19_brightness| has a LOWER PV base rate than the rest (0.130 against 0.140), and the gain is in the bulk (+0.0165, 25 of 28 folds) not the tail (+0.0041, 12 of 19).

The fitted coefficients are also self-consistent with the static model rather than with a generic change detector: the largest is d19_b11 at +1.88, and b11_mean is the static model's largest coefficient at +4.33. The block is reading "this roof moved TOWARDS the PV spectral signature", not "this roof moved".

In the units the atlas consumes (the same deployment step the reducer package was priced in): at an identical precision of 0.500 the flagged population goes 21,596 to 22,704 buildings and recall goes 0.6298 to 0.6621, +3.2 points, 5.1% relative. For comparison the whole noise-reduction package is +6.4 points, and that one needs the existing national imagery recomposited; this one needs a second epoch alongside it.

THE TWO LEVERS ARE NOT ADDITIVE, AND THIS IS THE RESULT THAT MATTERS FOR DEPLOYMENT. Everything above measures the epoch difference against the SHIPPED read path: a median composite read through CompositeIndex.read_window. The noise-reduction package (trimmed mean, area-weighted zonal means) replaces exactly that, and it was fitted on the same 30 quadrats. Re-running the whole ablation on the trimzonal table instead:

Base	Baseline within size band	Plus the 2019 difference	Gain	Folds better	Sign p
Shipped read path	0.8205	0.8388	+0.0234	25 of 29	0.0001
Noise-reduced (trimzonal)	0.8370	0.8510	+0.0047	20 of 28	0.036

Four fifths of the epoch gain is already captured by the noise reduction. It is still there, still significant, and the placebo is still null on the better base (-0.0017, 10 of 29) -- but a project that ships the reducer package should expect well under +0.01, not +0.023, from adding a second epoch on top.

The old epoch was frame-starved, and it is still not saturated. Those 2019/20 stacks carry a median 7 frames against the current 12. Re-pulling them over a wider window (2019-10-01 to 2020-04-30, --max-items 24 --max-cloud 50) more than doubles that to a median 16, and on the same noise-reduced base the gain rises and sharpens:

Old epoch	Median frames	Gain on trimzonal	Folds better	Sign p	Wilcoxon p
2019-11-01 to 2020-03-15	7	+0.0047	20 of 28	0.036	0.011
2019-10-01 to 2020-04-30, cap 24	16	+0.0067	23 of 29	0.002	0.0002

So the honest deployment number is +0.0067 and rising, not +0.0047, and the curve has not flattened -- a deeper old epoch is the cheapest remaining way to improve this, since it costs archive queries rather than a second national composite. One thing does change with the cleaner old epoch: the undifferenced 2019 LEVELS catch up almost completely (+0.0063, 24 of 29, p = 0.0005 against the difference's +0.0067). The "the roof cancels" prior was worth 6x on a noisy old epoch and is worth almost nothing on a clean one, which is what a regularisation prior should do: it buys most where the data is weakest.

The two are not measuring the same thing mechanically -- one cleans the current estimate, the other cancels the roof -- but they raise the same underlying quantity, the contrast between panel and roof relative to the noise on it, and once the measurement is clean there is less left for the difference to recover. The honest summary is that this register has found one large improvement available to roofclf, reachable two different ways, and not two large improvements. Best measured combination: trimzonal plus the 2019 difference at 0.8510 within size band against the shipped 0.8205, and the noise reduction is the cheaper half of it.

Caveats. The 2019 arm carries a median 7 frames against the current 12, so the difference is noisier on the old side than it needs to be; more 2019/20 scenes would likely widen the gain rather than close it. Rule-1 labels are epoch-relative, so an array installed between the mapping imagery and the composite is an unlabelled positive in both arms. Deploying this nationally means a second epoch of imagery for the whole country, which is the same cost open question 18 prices for the reducer change -- but unlike the reducer it buys new information rather than less noise, and it does not require recompositing what already exists. Scripts: scripts/compose_scene_stacks.py --window 2019-11-01:2020-03-15 --subdir stacks_2019, scripts/run_epoch_difference_ablation.py. Results: results/roofclf_epoch_difference.json, results/roofclf_epoch_mechanism.json.

Most footprints are one pixel (2026-09-20)

Every spectral feature roofclf ships is a zonal MEAN over the footprint. That is the right statistic only if a flagged roof is uniformly covered, and it is not: an array usually occupies part of a roof, so the mean is a mixture of panel and roof in an unknown proportion. This register's SNR budget says the roof half is the dominant noise term (roof-to-roof heterogeneity 0.0431 reflectance, 20 to 40x the sensor noise) and that the linear spectral limit ON THE MEAN is essentially reached. Order statistics are not on that axis. They are a different summary of the same pixels, not a cleaner estimate of the same summary, so the limit does not bind them.

Two hypotheses, registered before measuring. EXTREMES: the darkest pixel in a footprint approximates the panel endmember, so min and max are less diluted than the mean, and this should help MORE on larger footprints. SPREAD: a partially covered roof is internally heterogeneous and a uniform one is not, so the within-footprint standard deviation is direct evidence of partial cover. Both on four quantities (brightness, SPPI, SWIR/visible, blue/red), both free -- the window is already read and rasterized. Plus the control that matters here: the pixel COUNT alone, because a size proxy hiding inside either block would look like a win.

Block	AUC	Within size band	vs baseline	Folds better	Sign p
Baseline	0.8591	0.8093	--	--	--
Plus pixel count (control)	0.8579	0.8092	-0.0015	11 of 29	0.265
Plus extremes	0.8579	0.8074	-0.0006	12 of 29	0.458
Plus spread	0.8583	0.8080	-0.0012	11 of 28	0.345
Plus both	0.8575	0.8086	-0.0007	11 of 29	0.265

Nothing, and the diagnostic is the point: 72.4% of the 123,898 buildings in these 30 quadrats resolve to a SINGLE 10 m pixel. Median pixel count is 1 overall and 2 for a PV-bearing roof. For three quarters of the population min, max and mean are the same number and the standard deviation is exactly zero, so there is no within-footprint distribution to read. Splitting each fold's AUC by pixel count confirms it is not merely diluted: on the multi-pixel subset the blocks are still flat (spread +0.0009, 15 of 28 folds, p = 0.85; extremes -0.0003, 13 of 28, p = 0.85), so the registered size-conditional prediction fails on its own terms too.

This bounds a whole class of ideas, not just this one. Anything that needs structure INSIDE a footprint -- texture, panel-versus-roof segmentation, within-roof unmixing, a shape prior on the array -- has no substrate in three quarters of the population that roofclf exists to cover. It also explains retrospectively why area-weighted zonal means work (+0.0113 within size band, above): that change attacks the same fact from the other side, fixing how a footprint reads a pixel it only PARTLY covers rather than trying to read structure within one. Script: scripts/run_dispersion_ablation.py, feature block roofclf.DISPERSION_FEATURES, result results/roofclf_dispersion_baseline.json.

Verdict: one global threshold is already the right allocation, and the score is closer to calibrated across size bands than the per-quadrat rate spread suggests. Those are different claims about the same model -- rate error is a level error within a quadrat, and what a threshold needs is that the score ORDER the same way across strata, which it does. Nothing changes in the pipeline. Script: scripts/run_stratified_threshold.py, result results/roofclf_stratified_threshold.json.

Artifacts: results/roofclf_preprocess_ablation.json, results/subpixel_shifts.json, results/native_shifts.json, results/roofclf_gba_height.json.

Keeping more than the median composite (2026-09-19)

The composite reduces about twelve cloud-masked scenes to their per-pixel median (imagery.annual_composite). A median is the maximum-robustness central estimator, so it deletes the tail by construction, and the tail is where a specular surface at a fixed tilt should put its information. The scenes are already downloaded and then discarded, so the cheapest possible version of "use the time dimension" is to keep more of that distribution and ask whether it helps.

compose --stats now writes a temporal_stats sidecar beside each composite: per-pixel p10, p50, p90 and standard deviation for all ten bands, plus a valid-observation count. No extra network traffic, which matters because this stage is bandwidth-bound. What roofclf gets per building is the bright tail (p90 - p50, a free proxy for the glint that otherwise needs bespoke per-target scene pulls), the dark tail (p50 - p10, which should separate dark-and-static PV from dark-and-moving shadow), the temporal spread, and n_obs, without which spread is unreadable: std = 0 means "stable" at twelve looks and "one look" at one.

The measurement is a leave-one-quadrat-out ablation on the 30 production quadrats, and it reproduces the shipped fit exactly (123,867 buildings, 17,150 positives, median fold AUC 0.8574, 0.8206 within size band, min fold 0.4964), so the block is the only variable.

Feature block	AUC	Within size band
Size only	0.7441	0.5773
Size + temporal	0.7311	0.5868
Median composite only	0.8333	--
Size + spectral (shipped)	0.8574	0.8206
Shipped + temporal (compact, 6 columns)	0.8579	0.8232
Shipped + temporal (full, 31 columns)	0.8576	0.8262

It buys nothing. Paired per fold, the compact block moves AUC by a median +0.0003 (IQR -0.0013 to +0.0016), improving 16 of 30 folds, sign test p=0.57; within size band +0.0006 (IQR -0.0018 to +0.0033), 16 of 30, p=0.71. Widening from 6 columns to 31 does not rescue it, so the compact grouping is not what threw the signal away.

The temporal_only row is the part worth keeping, because it distinguishes "adds nothing new" from "says nothing". On its own the block sits at 0.5868 within size band against a 0.5773 size-only baseline: a trace above chance, not nothing, but nothing that survives having the median composite in the model. So the distribution's higher moments are close to redundant with its centre for this task, at this resolution.

Two things make this a fair test rather than a plumbing failure. The sidecar stores its own p50, and that band is bit-identical to composite_0.tif's median on 100% of valid pixels, so the temporal features and the existing reflectance features describe the same twelve scenes and no epoch confound is possible. And the block has real dynamic range rather than being flat: for B02 the median pixel spans 129 DN between p50 and p90, 183 DN between p10 and p50, with a standard deviation of 148 DN, against roof reflectances of order 1,000 to 3,000 DN.

The likeliest reason it fails is the same sensor ceiling that the 400 m² floor and the French transfer result keep returning: a sub-400 m² array is a minority of a 100 m² pixel, so whatever anisotropy it has is diluted by a roof that does not share it, in every statistic equally. Twelve dry-season scenes also give little specular opportunity, which is the ceiling glint already documents from the other direction.

Kept behind flags (compose --stats, roof-classifier --temporal-features), off by default, because re-measuring costs one afternoon and no retraining. Cost if it were ever wanted nationally: about 4x the per-cell composite disk, roughly 215 GB for Pakistan's 4,473 cells. Raw numbers: results/roofclf_temporal_ablation.csv (median per block) and results/roofclf_temporal_ablation_folds.csv (per fold, which is what the paired test above is computed from).

Temporal features for roofclf

Two candidate features were tested against the quadrats and neither was kept. Epoch jump, the change in the same classifier's score between two imagery epochs, measured at 0.8736 to 0.8736 AUC in its probability form (exactly zero effect) and 0.8736 to 0.8608 in its cheaper reflectance-delta form, with one quadrat failing outright. Step change, per-building aggregation of the pixel-level time-series signal below, did not survive a within-size-band control. An older design note proposed using the epoch comparison as a recall rescue rather than a feature; that specific plumbing was never built, and the measurement above is the reason it was not pursued.

Retraining on known failures

Two retrains aimed at documented failure modes, both negative in an instructive way.

The fraction-head quadrat-supervised retrain beat the production checkpoint in 13 of 13 quadrats in-sample and collapsed the scale dispersion from 11.95x to 2.48x, which looks decisive until the held-out quadrat comes back at 0.461 against an in-sample band of 1.878 to 3.684. A paired spatial block bootstrap puts the held-out AUC gain at -0.009 to +0.061, one-sided p 0.062. One quadrat and 797 labelled pixels is the binding constraint, so more holdout folds, not more analysis, is what would settle it.

The roofclf hard-negative retrain took the one concretely locatable confirmed false positive on record (six very bright roofs that both detectors flag) and folded them in as negatives. Six rows against 104,423 moved their own scores by 0.0001 to 0.003 and changed nothing else. Oversampling them 20x, 100x and 500x does eventually suppress them, but median_fold_auc falls 0.8824 to 0.8811 to 0.8726 to 0.839 on the way, so the model is learning to distrust one tiny neighbourhood of feature space at the expense of everything else. No factor tested threads that needle. The fix this actually recommends is mining more examples of the same bright-roof pattern nationally, for which no roofclf-side mining tool exists yet.

SPPI alone as Germany's sub-400 estimator (2026-09-18)

Germany's published sub-400 m² half uses no classifier: it prices roof area in the 200-400 m² band at a register-fitted constant, because that beat both roofclf and total roof area. The obvious thing left untried was the other detector. SPPI is a zero-training spectral index, it is already computed for every scored German building, and it is the corroborating half of Pakistan's AND-gate, so if any classifier were going to help here it is the one that needs no labels.

It does not. All four estimators below were run through scripts/validate_sub400_against_mastr.py on identical data, the same cells, the same municipalities and the same five-fold cross-validation, with only the weighting changed:

estimator	Gemeinden	median municipal error	best stratified	Spearman	slope
roof area only (published)	10,589	31.2%	28.8%	0.942	0.888
SPPI percentile rank	10,588	33.7%	31.0%	0.922	0.845
roofclf probability	10,005	36.8%	34.9%	0.855	0.333
SPPI top decile	10,209	49.9%	48.8%	0.824	0.498

Two formulations, because SPPI is a spectral index in roughly [-2.6, 0.07] rather than a probability and cannot be multiplied in raw. Percentile rank weights each roof by where its SPPI falls in the national distribution, the analogue of probability weighting. Top decile credits only roofs above the 90^th percentile, the analogue of thresholding and the way SPPI is used in Pakistan's AND-gate.

The interesting half of the result is that SPPI ranks German roofs better than the trained classifier does, on both error and slope, while needing no labels at all. The decisive half is that neither beats doing nothing. This is the same finding as the roof-area baseline, from a third direction: where PV is near-ubiquitous, capacity is close to proportional to roof area by construction, so an instrument that concentrates credit on a subset discards more real capacity than it correctly withholds. Thresholding is worst precisely because it discards the most.

The baselines here read 31.2% and 36.8% where Germany's page records 35.4% and 48.4%, because the grid was rebuilt without the building-density filter (4,871 cells against 4,657) after those were measured. The ordering is unchanged and the within-run comparison is what the table is for.

A NaN trap worth recording, because it produced a confident wrong answer rather than an error. The first top-decile run reported zero municipalities and zero capacity. 2,660 of 6.2M buildings, 0.043%, carry no SPPI value; np.quantile returns NaN if any input is NaN, and x >= NaN is False for every row, so the threshold flagged nothing and the harness dutifully reported zeros all the way through. The harness is now NaN-safe and logs how many buildings lack a value.

The partial result worth watching

Time-series step detection below the floor

At a median installation area near 50 m², the trained model reads 0.000 probability on 99.8% of true footprints and glint validates zero of 1,021. Static appearance is exhausted. But appearance in time is a different signal: a panel installed in 2023 is a step change in a dense per-pixel Sentinel-2 series, even if no single scene shows it.

scripts/pv_step_signal.py removes common-mode atmosphere against reference pixels, guards co-registration by phase correlation, learns the PV installation change vector spectrally rather than assuming a fixed index, deseasonalises with annual harmonics and per-orbit offsets, and scans for the best breakpoint per pixel.

The good part: Area under the ROC curve of 0.875 and 0.74 on held-out halves, against 0.50 for the model on the same footprints. That is the first non-zero discrimination anyone here has achieved below 500 m².

The part that failed: Converting that into a city-scale unmapped-capacity number was rejected by its own control. The method's estimate of unmapped area per built-up pixel sat inside the false-area floor measured on two PV-free cropland control cubes, so nothing in its totals block is quotable as capacity. What survives is the ranking: step-leads are defensible as a lead ordering.

Two landmines are documented for anyone continuing: a propensity confound (the households that install panels differ systematically from those that do not, in ways visible from space) and duplicated Sentinel-2 baseline products that silently double-count dates.

Deeper write-ups

These pages hold the full tables and derivations behind the rows above. Each one carries a status banner stating where it now stands relative to the current pipeline, because several of them were written before the thing they describe was replaced.

Write-up	Subject
Calibration boxes	The running log of every ground-truth area, how each was mapped and what it changed
roofclf national deployment and temporal features	The first national scoring run, and the epoch-jump and step-change feature tests
roofclf cell-edge false positives	Two compounding bugs that made 45.6% of national flags artifacts, and both fixes
Quadrat-supervised fraction retrain	The in-sample win, the holdout that did not support it, and the block bootstrap
Fraction-head hard-negative retrain	A regime-specific win: better on large arrays, worse on dense small rooftops
SPPI spectral index evaluation	What SPPI can and cannot do, and why it is used only as a second opinion
Boom-window stacking	Why the 2021-versus-current retrain is inconclusive rather than negative
Standard-pose matched filter	Assessed against real pose data and not recommended
Glint PSF and matched filtering	The measured Sentinel-2 glint PSF, why filtering on it loses to the aperture statistic, and the verified-negative false-spike rate
Glint spike-rate estimator	Glint as a statistical density estimator, and the floor it hits
Glint tile-batched coverage	The 22x speedup, and the silent scene-loss bug it uncovered
Glint-validated training labels	A proposal to feed corroborated detections back as labels
Quadrats as training data	The proposal that became roofclf
Calibration imagery dating	Why ground-truth completeness is relative to an imagery date, and what closing that costs