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Geographic split audit
Canonical source: versioned documentation. This wiki copy is maintained manually.
The complete imported PV4GER-SEG release was split using
geographic-block-buffer-v1, seed 20261020, 10 km
metric cells and a 1 km buffer. The seed and policy were fixed before any model
training or evaluation. The
machine-readable report contains the fingerprints,
source coordinate hash and policy parameters. No imagery or per-sample labels
are included in this repository.
| Partition or exclusion | Samples |
|---|---|
| Training | 3,282 |
| Validation | 278 |
| Test | 421 |
| Unassigned: geographic buffer | 38 |
| Unassigned: upstream zero mask pending review | 8 |
| Total | 4,027 |
The policy uses 3,628 linked source components and 360 geographic blocks. The minimum measured centre distances are:
| Pair | Metres, EPSG:25832 |
|---|---|
| Training ↔ validation | 1,012.82 |
| Training ↔ test | 1,010.49 |
| Validation ↔ test | 1,146.68 |
The final snapshot validates with no errors. Its 12,081 warnings preserve the
known missing acquisition dates, missing original georeferences and upstream
annotation status. Source pixels are unchanged. Repeating the split with the
same parameters produced the same canonical dataset fingerprint:
597b9facb7f115448e81af3b88de7e1081d510c38108c294195de05763d841c5.
This is a buffered internal holdout from one source. It is not a new independent benchmark, does not exclude all possible city/campaign correlations, and cannot prove independence from existing pretrained models. Local label review and additional reviewed hard negatives remain outstanding. No model performance has been measured on this test partition.
Developed and maintained by Valeford under the Spacement brand. Original project code: Apache-2.0. See the repository NOTICE for attribution.
Spacement PVExtent
- Home
- PVExtent Vidda vs. Tinden (v1.0 Releases)
- Getting started
- Dataset format
- Annotation guide
- PV4GER-SEG import
- Import audit
- Geographic splits
- Split audit
- Label review
- NRW candidates
- Bulk NRW training imagery
- Bulk mask curation
- Live training monitor
- Training quality exclusions
- Final bulk mask audit
- Experimental negative curation
- Negative curation audit
- Exploratory negative holdout
- Negative holdout audit
- Exploratory positive holdout
- Positive holdout audit
- Training smoke test
- Smoke audit
- Pretrained baseline training
- Baseline comparison
- Baseline comparison audit
- Six-epoch continuation audit
- V4 bulk training audit
- Domain-balanced training audit
- DeepLabV3+ ASPP architecture audit
- DeepLabV3+ Focal + Lovász loss audit
- DeepLabV3+ V5 Photometric Invariance audit
- CAD vectorization & rectilinear audit
- Test-Time Augmentation (TTA) audit
- Probability calibration & temperature scaling audit
- Seamless overlapping tiled inference & Hann blending audit
- Heterogeneous ensemble & quality triage audit
- Sealed 421 test benchmark audit
- 3D elevation & solar yield audit
- Multi-temporal 4D change detection audit
- Multispectral NIR & spectral signature audit
- Encoder weights and licenses
- Baseline audit
- Frozen validation errors
- Validation error audit
- Candidate predictions
- Research roadmap
Project