Dhruv1000/TSRDA

T-SRDA — current results and reproducible research

Publication update — 12 October 2026. Highest directly evaluated full-method Test mIoU: 60.4429% (validation 65.8657%). Primary highest-validation-selected method: 66.3467% validation / 59.9973% test.

The new improvement comes from spatial statistics of frozen optical temporal features, not further encoder fine-tuning. The 60.4429% method adds a 5×5 local feature mean and standard deviation to every 256D pixel vector, fits a training-only 768D shrinkage-covariance Gaussian classifier, and blends it with the preceding 59.0010% ensemble. The validation-selected method instead performs three rounds of feature-affinity probability diffusion. The difference in selection status is retained throughout this release.

Reviewer-readiness audit: publication risks, completed audits, remaining controls and the recommended defensible claim. The standalone local-moments score was recovered as a post-hoc diagnostic (60.4573% test mIoU); the highest directly evaluated full method remains the 60.4429% blend. Neither is an independent test confirmation.

Current detailed release report · All 63 context experiments · Per-class scores and uncertainty · Current inference guide

Method	Validation mIoU %	Test mIoU %	Interpretation
Historical foundation ensemble	64.1505	58.6346	Historical validation-selected control
Quadratic readout, eta 0.05	64.4319	58.4853	Rejected: lower test score
Covariance shrinkage 0.03	64.3801	58.5651	Rejected: lower test score
5×5 neighborhood mean	65.3036	59.0010	Parent spatial baseline
Entropy-weighted diverse ensemble	64.6969	58.4377	Rejected: lower test score
Three-pass feature-affinity diffusion	66.3467	59.9973	Primary highest-validation selection
Mean of affinity and moments branches	66.2347	60.4334	Tested validation-promoted alternative
5×5 feature mean + standard deviation, blended	65.8657	60.4429	Highest directly evaluated full method
Standalone 5×5 local moments, recovered from saved blend	65.6812	60.4573	Post-hoc diagnostic; not independent confirmation
Summer-crop conditional specialist	65.6879	59.2033	Tested validation-promoted alternative

The supervised STCLN configuration remains 76 ordered training entries (72 unique, fold 1), 76 ordered validation entries (71 unique, fold 2), seed 3407, and two fixed 32×32 crops per entry: [0:32,0:32] and [32:64,32:64]. Duplicates remain included. There are 99,345 scored training pixel entries and 93,361 scored validation pixel entries, ten Sentinel-2 bands and twenty output columns. Metrics score ground-truth classes 1–18, excluding ground-truth 0/19. Full test evaluation uses 482 fold-4 native 128×128 patches and 3,961,114 scored pixels.

This matches the downloaded reference's supervised dataset configuration, not every experimental resource or evaluation choice. TESSERA is externally pretrained/distilled and uses acquisition-calendar inputs; its geographic pretraining overlap is uncertified. Seed 3407 does not imply an identical optimization trajectory or upstream pretraining seed. Context is restricted to each original 32×32 crop during fitting/validation and spans full 128×128 patches at test time. Repeated inspection of the same test fold makes these exploratory results. Best observed test performance must not be presented as a pristine validation-selected benchmark.

Preserved history through 11 October

The following sections describe the historical 58.6346% system, retained as a component and reproducible notebook. Current scores and recipes are above.

T-SRDA — satellite time-series crop mapping

Latest audited result: 58.6346% Test mIoU · 64.1505% validation mIoU
Updated 11 October 2026. +8.3883 test percentage points over the preceding 50.2463% incumbent.

Main method, architecture and code · FINAL REPORT · All results · Ablations · Publication report

Complete executable notebook

Open the full pretraining → fine-tuning → 58.63% ensemble notebook · Download .ipynb

The notebook includes the actual atrous/SRDA pretraining source, a place to attach your encoder checkpoint, complete fine-tuning and readout code, all ensemble component paths, and fresh raw-data inference. It has 88 cells with detailed markup, comments and embedded visuals. The earlier DaViT/Swin encoder was replaced by Temporal Spatial-Reduction Dual-Attention in the supplied checkpoint; TESSERA's optical encoder remains frozen. Notebook guide and validation.

What actually improved the result?

We added frozen pretrained TESSERA optical temporal-Transformer features, fitted a Gaussian classifier with shared shrinkage covariance on the original training crops, and averaged its probabilities with our existing ensemble. The new encoder was pretrained/distilled by TESSERA's authors and was not fine-tuned here. Alone, this new branch achieves 57.1841% test mIoU; the validation-selected 50/50 blend reaches 58.6346%.

The earlier “above + new CNN control” remains inside the incumbent: it is a separately trained small temporal CNN with dilated convolutions and masked mean/max pooling, added to the spatial–temporal network, a cosine class-cluster classifier and an older temporal CNN. SAM means Sharpness-Aware Minimization, not Segment Anything. The main-method walkthrough explains the full chain, including that CNN.

Comparison scope: supervised sample lists, duplicates, folds, crop coordinates and local numerical seed match the downloaded STCLN code. This is an external-pretraining + acquisition-calendar track, not an identical-pretraining-budget STCLN comparison. External pretraining geographic disjointness is not certified. Checkpoint-specific normalization, temporal padding, selection criteria, ensemble size and confusion-matrix conventions also differ. Repeated test inspection makes the project exploratory; validation-frozen promotion in this round does not erase that history.

Measured results

Configuration	Validation mIoU	Test mIoU	Status
Original SAM optimizer network	50.0067%	47.5001%	Historical single-network baseline
Spatial network + cluster readout + old/new temporal CNNs	52.5640%	49.4555%	Historical validation-selected reference
Reference + Gaussian readout + structured self-supervised branch	53.3274%	50.2463%	Incumbent before foundation transfer
Frozen pretrained optical encoder + Gaussian readout alone	63.1343%	57.1841%	New standalone branch
50% incumbent + 50% pretrained optical branch	64.1505%	58.6346%	New validation-selected recommendation
50% incumbent + missing-radar fused pretrained branch	61.1794%	57.1967%	Tested alternative
Matched random optical encoder alone	35.0747%	—	Rejected on validation
Matched random fused encoder alone	26.7142%	—	Rejected by promotion gate

Final test metrics: pixel accuracy 84.8792%, macro F1 70.9305%, kappa 0.814272. Full 482-patch fold-4 evaluation; 3,961,114 scored pixels. Independent audit.

Dataset configuration preserved

Item	Verified configuration
Local fine-tuning / fitting seed	3407; not a claim of identical RNG trajectories or external pretraining seed
Training	76 ordered entries, 72 unique, fold 1; upstream duplicates retained
Validation	76 ordered entries, 71 unique, fold 2; upstream duplicates retained
Training / validation crops	Two fixed 32×32 windows: [0:32,0:32] and [32:64,32:64]; 152 crop entries per full pass
Classifier fit	99,345 foreground training pixel entries, including duplicated entries
Test	482 unique fold-4 patches, native 128×128, batch 1
Bands / labels	10 Sentinel-2 bands; 20 output columns; score classes 1–18, ignore ground truth 0 and 19
Test-time augmentation	None in this scored recipe
Original self-supervised pool	496 fold-5 patches; external TESSERA has its own different pretraining data

Exact lists and crop manifest · Fresh STCLN recheck. This is a same-supervised-sampling comparison, not a controlled claim of beating STCLN under equal external pretraining.

Architecture and training history

The supplied checkpoint supplies the spatial–temporal branch. Fine-tuning and subsequent cluster/CNN/statistical/structured-self-supervised experiments build the 50.2463% incumbent. The new frozen optical encoder adds a stronger per-pixel representation of seasonal multispectral trajectories. A fitted Gaussian classifier maps these features to eighteen crop classes. The old ensemble retains complementary context.

P_final = 0.50 P_incumbent + 0.50 P_TESSERA_optical_Gaussian
P_incumbent = 0.65 P_reference + 0.25 P_original_Gaussian + 0.10 P_structured_SSL
P_reference = 0.28125 P_SAM_optimizer + 0.28125 P_cosine_class_clusters
            + 0.1875 P_old_temporal_CNN + 0.25 P_new_temporal_CNN

These are nested probability averages, not logit averages. Preserve the nesting to reproduce floating-point arithmetic. SAM means Sharpness-Aware Minimization, not Segment Anything. Class clusters are several training-derived representative feature vectors per crop class, not extra semantic classes.

The GIF is explanatory, not a recording of training. See the detailed architecture, input adaptation and classifier mathematics.

Repository guide

main_method/
  README.md                           # Full method, mathematics, code map, reproduction
  code/                               # Portable current optical transfer + original model code
  configurations/32X32_tessera_transfer_seed3407/
  checkpoints/32X32_tessera_transfer_seed3407/
  checkpoints/shared/                 # Earlier ensemble components, preserved
ablations/
  01_... through 09_...                # Historical experiments, preserved
  10_plateau_breakthrough/workspace/   # ConvNeXt controls, all later rounds, logs/weights/caches
reports/oct11/                        # Fresh metrics, audits, CSV/JSON experiment ledger
docs/                                 # Research rationale, release report, glossary, histories
visuals/                              # New architecture, scores, per-class plot and schematic GIF
FINAL_REPORT.md                        # Full consolidated current and historical report
RESULTS.md                            # Results navigation

The original pretrained folders were not modified. All prior HF files remain. New negative results and checkpoints are retained rather than silently dropped. Read-only historical snapshot READMEs keep their original wording; active indexes link to the current result.

Reproduce and inspect

export PASTIS_ROOT=/path/to/PASTIS
python main_method/code/foundation_transfer.py verify
python main_method/code/foundation_transfer.py evaluate-cached --check-raw-labels --output /tmp/tsrda-replay
python main_method/code/foundation_transfer.py extract --split val --output /tmp/tessera-val

The first checks checkpoint hashes. The second independently replays the complete result from saved probabilities and checks raw targets. The third performs fresh optical-branch inference, not the full ensemble. See reproduction boundaries and full commands. The original portable evaluator remains for the historical 49.4555% reference.

Research evidence and remaining weaknesses

Seventeen classes improve. Largest gains include Sunflower +25.38, Winter barley +24.64, Winter triticale +17.32 and Mixed cereal +15.95 IoU points. Sorghum falls −5.62. Orchard, Sorghum, Winter triticale and Mixed cereal remain difficult.

Scratch class-query models overfit, short reconstruction SSL did not help them, calendar-only CNN changes did not beat the incumbent, and spatial refinements produced tiny validation gains that did not transfer to test. The matched random optical encoder obtains only 35.0747% validation versus 63.1343% pretrained. The decisive evidence supports representation transfer, not a generic optimizer trick or a proof of one particular pretraining objective. Detailed research interpretation.

Runtime, attribution and history

The final transfer pilot queue took 5.01 minutes, with cached incumbent predictions; this excludes download, implementation, independent audit and historical model training. See timings and limitations.

• TESSERA original research.
• TESSERA v2 research.
• Official TESSERA project.
• Pinned small-student release.

The bundled upstream model card states that the paper's reported student benchmarks use a different teacher release; they are not expected scores for this checkpoint. Our PASTIS scores are measured locally. The external model card declares CC0; original authors and provenance are retained.

Earlier detailed explanations and reports remain under history and the ablation index. The original artifact inventory describes the previous release; the new release inventory adds this update.