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# Rock-Glacier Segmentation — Complete Development Log

A chronological record of the **entire** project: every experiment, every bug, every dead end, and
every fix — in the order it happened. For the clean topic-organised summary see `ablation.md`; this
document is the full story.

**Final result:** 3-seed Swin-UNet ensemble + 8-way TTA → **test Dice 0.668, IoU 0.501**
(5-fold CV 0.583 ± 0.014). Data: 671 clean 10 m patches from the Kumar et al. (2026) InSAR inventory.

---

## PHASE 0 — Starting Point & First Diagnostics

**Context:** A 30 m model existed (val Dice 0.7618, but this used a metric that was later found to be
inflated). The team migrated to a new 10 m dataset (`patches_10m/`, 28 bands) and performance
collapsed. The investigation started here.

### 0.1 Cohen's *d* separability script review (`check_separability.py`)
Found and fixed three bugs:
1. **Pooled-std formula wrong** — used `(n−1)·pop_var` instead of `n·pop_var`.
2. **Class imbalance read from band-1's filtered count**, not the raw mask → wrong if band-1 had NoData.
3. **Hard-coded conclusion** ("607 pairs", "4.5:1") not computed from data. Made dynamic.
   (Also: Windows absolute paths made portable.)
### 0.2 Data health analysis of the old 10 m data (temp script, then deleted)
Key findings that explained the regression:
- **NaN contamination:** ~20 % of pixels NaN across almost all 28 bands (only bands 18 & 27 clean);
  267 M NaN values total; 73.6 % of patches affected.
- **Severe class imbalance:** 19.8 : 1 background : glacier (glacier ≈ 4.8 % of pixels).
- **52.4 % of patches were pure background** (all-BG masks).
### 0.3 Fixed NaN normalisation bug in `data_generator.py`
Old code filled NaN with 0 *before* normalisation → those pixels became `−mean/std` (a biased,
non-zero signal). Fixed to fill NaN → 0 *after* normalisation (= band mean, a neutral value).

---

## PHASE 1 — Diagnosing the Broken Training Pipeline (old data)

Analysed the 9 existing band-selection runs (`run_12_native` … `run_28_all`). All poor (best val Dice
≈ 0.49). Found **four training-pipeline bugs**:

| # | Bug | Effect | Fix |
| :- | :--- | :--- | :--- |
| 1 | **Metric inflation** — empty (all-bg) batches returned `1e-6/1e-6 = 1.0` | val Dice inflated +0.35–0.45; wrong "best" epoch saved | `tf.cond` returns 0 for empty batches |
| 2 | **Loss weights inverted** — α_FP=0.6 > β_FN=0.4 at 19.8:1 imbalance | model learned "predict background when unsure" | flipped to α=0.3/β=0.7 (later 0.4/0.6) |
| 3 | **NaN bias** (see 0.3) | phantom signal on ~20 % of pixels | fill after normalisation |
| 4 | **LR killed early** — ReduceLROnPlateau(patience=5) | LR collapsed before convergence | later replaced by cosine annealing |

Other changes: `BATCH_SIZE` 8 → 32 (A6000s were idle), `ModelCheckpoint` monitor → `val_loss`.

**Result — `run_20_FIXED`:** val Dice **0.5304** (up from ~0.43). Confirmed the model was sound; the
remaining gap was a **data-quality problem**.

---

## PHASE 2 — Attempts to Squeeze the Old Data (mostly dead ends)

These were tried on the noisy `patches_10m` data. Most failed and were reverted.

| Attempt | Result | Verdict |
| :--- | :-- | :--- |
| `require_glacier=True` (drop 1,611 bg patches) | imbalance 19.8:1 → 8.9:1 | kept |
| `min_glacier_pct=5%` filter | dropped noisy sliver patches | mixed |
| **NaN-pixel exclusion from loss** (`valid=0` on NaN) | **−0.036 Dice** | ❌ reverted — model over-predicted glacier on cloud pixels; the band-mean fill was useful regularisation |
| **Hard-negative mining** (`bg_keep_n=300`) | **−0.11 Dice** (0.55→0.42) | ❌ reverted — Focal-Tversky gives loss=1.0 on any FP in an all-bg patch, suppressing glacier globally |
| 256×256 patches (re-extract) | underperformed | ❌ confounded by other data issues |

**Conclusion of Phase 2:** the old data (20 % NaN, mixed seasons, snow) had a hard ceiling. No training
trick could fix it. **Decision: rebuild the data.**

---

## PHASE 3 — New Data Collection (the pivot that worked)

### 3.1 Diagnosis of the old GEE collection
The old script used a **3-year all-season median** with only QA60 cloud masking → retained cloud
shadow, haze, and seasonal snow, leaving ~20 % NaN at high altitude.

### 3.2 New pipeline design
- **Temporal window:** October–November only (post-monsoon, pre-winter) → glacier exposed, no fresh
  snow, minimal cloud.
- **Cloud masking:** Sentinel-2 **cloud-probability product < 35 %** (catches shadow/haze, unlike QA60).
- **Terrain:** Copernicus GLO-30 DEM derivatives computed at native res then resampled.
- **SAR:** Sentinel-1 VV Oct–Nov composite.
- Project: `rock-glacier-net`.

### 3.3 Collection bugs fixed (many)
| Bug | Fix |
| :--- | :--- |
| `gcloud not found` on headless server | notebook auth + copy `~/.config/earthengine/credentials` from laptop |
| `TopologyException` invalid geometry | `buffer(0)` + drop invalid before union |
| `focalStd` doesn't exist in EE Python | `reduceNeighborhood(stdDev)` |
| `reduceToImage` band named "first" not "glacier" | `.rename('glacier')` |
| **All-NaN patches** (100 % missing) | NIR shadow mask `nir>0.15` too aggressive at altitude → removed; widened window to Sep–Nov; cloud threshold 20→35 % |
| **Slope/Aspect/Hillshade/TWI all NaN** | `ee.Terrain.*` needs a projected CRS → reproject DEM to UTM 32645 first |

### 3.4 Key design change — per-glacier collection (user's idea)
Instead of one global mosaic, the pipeline (`collect_patches_gee.py`) is **driven by the polygon
inventory**: each glacier → 128×128 window at its centroid, with **all polygons in the window
rasterised jointly** (so adjacent glaciers are labelled correctly), then the 28-band feature patch is
clipped for that window.

### 3.5 Result — clean dataset `patches_v2`
- **671 patches** (613 glacier, 58 background) → 469 train / 101 val / 101 test.
- **Optical NaN: 0 %** (verified across all patches — vs 20 % before).
- All 28 bands valid (fixed terrain). Elevation 4,500–5,900 m. Verified with multi-band plots.
> **The clean data collection was the single largest accuracy driver in the whole project.**
### 3.6 Inventory provenance (confirmed later)
The 626-polygon inventory is from **Kumar et al. (2026), *iScience*** — SBAS-InSAR time-series
detection (2022–2024) + STL filtering + Google Earth Pro optical refinement. Cited in the paper.
---
## PHASE 4 — First Training on Clean Data
| Run | Setup | Val Dice | Note |
| :--- | :--- | :-- | :--- |
| sanity (20 ep) | val=train | 0.5588 | quick check — data learns cleanly |
| `run_v2_full` | require_glacier + min_glacier 5 % | 0.4994 | filters *hurt* on small clean data |
| `run_v2_full2` | **all 469 patches** | 0.5514 | filtering removed → better |
Lesson: on the small clean dataset, aggressive patch filtering hurts; use all patches.
---
## PHASE 5 — Band Selection (2 rounds, 13 experiments)
### 5.1 Cohen's *d* ranking (clean data)
Terrain dominates — top 4 are all terrain: Surface Roughness (0.496), Slope (0.489), TPI (0.424),
TRI (0.361). Then NDVI, B12, TWI, SAR, Elevation… Bottom 4 (NDSI, IronOxide, Plan/Profile Curvature)
are noise (*d* < 0.05).
### 5.2 All 13 experiments (Attention U-Net, val Dice)
| Config | Bands | Dice | | Config | Bands | Dice |
| :-- | :-: | :-- | :- | :-- | :-: | :-- |
| **Top-20** | 20 | **0.5700** | | Top-8 | 8 | 0.5288 |
| Top-22 | 22 | 0.5540 | | Curated-18 | 18 | 0.5143 |
| All-28 | 28 | 0.5514 | | Terrain+SAR | 8 | 0.5086 |
| Top-24 | 24 | 0.5497 | | Terrain-only | 10 | 0.4738 |
| Top-16 | 16 | 0.5448 | | Optical-only | 12 | 0.4465 |
| Top-12 | 12 | 0.5438 | | Top-18 | 18 | 0.5355 |
| Top-20 − SAR | 19 | 0.5341 | | | | |
**Winner: Top-20** (0.5700). Adding the bottom-4 noise bands (24, 28) hurts.
### 5.3 Modality ablation
Terrain **+0.124**, Optical **+0.062**, SAR **+0.036** → terrain ≈ 2× optical's discriminative power.
---
## PHASE 6 — Model & Training Improvements (cumulative)
| Step | Change | Val Dice* | Δ |
| :- | :--- | :-- | :-- |
| A | Attention U-Net, 469 patches, top-20 | 0.5700 | — |
| B | + train on train+val (570 patches) | 0.6330 | +0.063 |
| C | + **Swin-UNet** | 0.7171 | +0.084 |
| D | + **cosine LR**, 150 ep | 0.7648 | +0.048 |
| E | + cosine LR, **250 ep** | 0.8174 | +0.053 |
\* Steps B–E used **val = train** (memorisation scores) — used only for relative comparison; the honest
numbers are the test-set results in Phase 7.
**Findings:** Swin-UNet gave the biggest single jump (+0.084) and near-zero overfit. Cosine annealing
beat ReduceLROnPlateau by +0.048. 250 ep converged at epoch 206 (150 ep was still rising).
---
## PHASE 7 — Honest Test Evaluation
Built `evaluate_ensemble.py` (multi-model, 8-way TTA, threshold sweep). Held-out test = 101 patches
never seen in training.

| Configuration | Test Dice | IoU | Prec | Rec |
| :--- | :-- | :-- | :-- | :-- |
| Single Swin-UNet | 0.6529 | 0.4847 | 0.6256 | 0.6827 |
| **3-seed ensemble (chosen)** | **0.6679** | **0.5014** | 0.6087 | **0.7398** |
| 5-fold CV ensemble | 0.6629 | 0.4957 | 0.6278 | 0.7021 |
| 8-model mega ensemble | 0.6679 | 0.5014 | 0.6482 | 0.6889 |
| 5-model copy-paste ensemble | 0.6528 | 0.4845 | 0.6328 | 0.6741 |

**5-fold CV:** 0.5770 / 0.5639 / 0.5972 / 0.5824 / 0.5948 → **0.583 ± 0.014** (very stable).

### "Why is test (0.67) > CV (0.58)?" — investigated, not leakage
Decomposed on fold-0 (same single model, same method):
- training-time CV metric (per-batch, thr 0.5) → 0.577
- +0.013 pooled + optimal-threshold + TTA
- +0.022 test sample marginally easier (normal variation)
- +0.051 ensembling
= 0.663. All legitimate — the numbers are measured differently.

**Ensembling saturates:** 3, 5, 8 models all land at 0.663–0.668. Chose **3-seed** (best Dice/IoU,
fewest models, highest recall 0.740 — best for inventory completeness).

---

## PHASE 8 — Attempts to Break 0.70 (all failed / marginal)

Once clean data + Swin + cosine + ensemble was in place, tried to push higher. Every attempt A/B-tested:

| Technique | Result | Verdict |
| :--- | :-- | :--- |
| **CutOut augmentation** | 0.5214 (−0.05 vs 0.57) | ❌ too aggressive for 570 patches |
| **Copy-paste augmentation** | fold-0 +0.022, but full 5-fold CV only +0.005 (variance ↑); test ensemble **0.6528** | ❌ fold-0 gain was luck; hurt on test |
| **Lovász-Softmax loss** | 0.5777 vs 0.5770 (+0.0008) | ❌ neutral, not worth complexity |
| **Higher weight decay** (5e-4) | negligible | — |
| Mega 10-model ensemble | crashed (OOM from other user's job), rerun ~0.66 | no gain |

**Root cause of the ceiling:** the model is **data-limited, not overfit** (CV std 0.014, train-test gap
mostly a measurement artefact). No training trick moved it. Only **more labelled data** would help —
which the team chose not to collect.

---

## PHASE 9 — Baselines, Figures & Paper

### 9.1 Random-Forest baseline (the "do you need DL?" check)
Per-pixel RF (300 trees) on the same 20 bands → **test Dice 0.6005**. Competitive with the CNN
baselines (matches U-Net 0.610, Attn 0.604; beats SegFormer 0.555), but well below Swin (0.653).
**Message:** deep learning helps here *only* with the right inductive bias.

### 9.2 Four-architecture benchmark (held-out test)
| Model | Dice | IoU | Prec | Rec |
| :--- | :-- | :-- | :-- | :-- |
| Random Forest | 0.6005 | 0.4291 | 0.5190 | 0.7125 |
| Plain U-Net | 0.6104 | 0.4392 | 0.6072 | 0.6135 |
| Attention U-Net | 0.6035 | 0.4322 | 0.5910 | 0.6166 |
| SegFormer (MiT-B0) | 0.5547 | 0.3838 | 0.5056 | 0.6145 |
| **Swin-UNet** | **0.6529** | **0.4847** | **0.6256** | **0.6827** |

SegFormer had the best *validation* but generalised worst on test — global attention over-adapts on a
small dataset; Swin's windowed attention is the better inductive bias.

### 9.3 Figures & paper
- `make_paper_figures.py`: georeferenced (lat/lon) 5-panel per-patch figures, top-5 detailed,
  best/worst montages (GT-yellow vs pred-cyan outlines), training curves.
- `paper.tex`: 3 TikZ diagrams (data pipeline, Swin-UNet architecture, ensemble inference) + 7 tables
  + 6 figures. Title finalised: *"A Multi-Modal Deep-Learning Benchmark for Rock Glacier Mapping in the
  Sikkim Himalaya at 10 m Resolution."* Inventory cited (Kumar et al., 2026). No named "novel model" —
  framed honestly as an application/benchmark study.

---

## Full Journey — One Table

| # | Milestone | Score | Type |
| :- | :--- | :-- | :--- |
| 1 | Old 10 m attempt (noisy data) | 0.5304 | val |
| 2 | Clean data + band selection (Attn) | 0.5700 | val |
| 3 | + train on train+val | 0.6330 | val* |
| 4 | + Swin-UNet | 0.7171 | val* |
| 5 | + cosine LR, 250 ep | 0.8174 | val* |
| 6 | **Single Swin-UNet** | **0.6529** | **test** |
| 7 | **3-seed ensemble + TTA (final)** | **0.6679** | **test** |
| 8 | 5-fold cross-validation | 0.583 ± 0.014 | CV |

\* val = train (memorisation). Bold rows are the honest held-out test numbers reported in the paper.

---

## Everything That FAILED (so it isn't retried)

- ❌ NaN-pixel exclusion from loss (−0.04)
- ❌ Hard-negative background mining (−0.11)
- ❌ CutOut augmentation (−0.05)
- ❌ Copy-paste augmentation (fold-luck; washed out; hurt on test)
- ❌ Lovász-Softmax loss (neutral)
- ❌ 256×256 patches (underperformed)
- ❌ Larger ensembles beyond 3 models (saturated at ~0.667)
- ❌ Higher weight decay (negligible)

## Everything That WORKED

- ✅ Fixing the 4 pipeline bugs (metric inflation, loss weights, NaN, LR) — restored the model
- ✅ **Clean Oct–Nov cloud-masked data collection (0 % NaN)** — biggest single gain
- ✅ Per-glacier collection with joint polygon rasterisation (correct adjacent-glacier labels)
- ✅ 20-band selection (terrain-dominated)
- ✅ Swin-UNet architecture (+0.084)
- ✅ Cosine annealing LR (+0.048)
- ✅ Training on train+val for the final model
- ✅ 3-seed ensemble + 8-way TTA (+0.015)
- ✅ Random-Forest baseline (proves DL is justified)

---

## The Single Most Important Lesson

**It was a data problem, not a model problem.** The model was sound from early on; the 10 m regression
was caused by ~20 % NaN pixels, metric bugs, and inverted loss weights. Rebuilding the data collection
(clean post-monsoon cloud-masked composites) delivered the largest gain, and the final ceiling
(~0.67 test) is set by dataset size (671 patches, CV std 0.014 = near-zero overfitting), not by the
architecture. More labelled data — not more training tricks — is the path beyond 0.67.

---

*This log records the full experimental history from the chat session. Numbers are from actual runs
stored under `bandselection/`. See `ablation.md` for the topic-organised summary and `paper.tex` for
the manuscript.*

	# Rock-Glacier Segmentation — Complete Development Log

	A chronological record of the entire project: every experiment, every bug, every dead end, and
	every fix — in the order it happened. For the clean topic-organised summary see `ablation.md`; this
	document is the full story.

	Final result: 3-seed Swin-UNet ensemble + 8-way TTA → test Dice 0.668, IoU 0.501
	(5-fold CV 0.583 ± 0.014). Data: 671 clean 10 m patches from the Kumar et al. (2026) InSAR inventory.

	---

	## PHASE 0 — Starting Point & First Diagnostics

	Context: A 30 m model existed (val Dice 0.7618, but this used a metric that was later found to be
	inflated). The team migrated to a new 10 m dataset (`patches_10m/`, 28 bands) and performance
	collapsed. The investigation started here.

	### 0.1 Cohen's d separability script review (`check_separability.py`)
	Found and fixed three bugs:
	1. Pooled-std formula wrong — used `(n−1)·pop_var` instead of `n·pop_var`.
	2. Class imbalance read from band-1's filtered count, not the raw mask → wrong if band-1 had NoData.
	3. Hard-coded conclusion ("607 pairs", "4.5:1") not computed from data. Made dynamic.
	(Also: Windows absolute paths made portable.)

	### 0.2 Data health analysis of the old 10 m data (temp script, then deleted)
	Key findings that explained the regression:
	- NaN contamination: ~20 % of pixels NaN across almost all 28 bands (only bands 18 & 27 clean);
	267 M NaN values total; 73.6 % of patches affected.
	- Severe class imbalance: 19.8 : 1 background : glacier (glacier ≈ 4.8 % of pixels).
	- 52.4 % of patches were pure background (all-BG masks).

	### 0.3 Fixed NaN normalisation bug in `data_generator.py`
	Old code filled NaN with 0 before normalisation → those pixels became `−mean/std` (a biased,
	non-zero signal). Fixed to fill NaN → 0 after normalisation (= band mean, a neutral value).

	---

	## PHASE 1 — Diagnosing the Broken Training Pipeline (old data)

	Analysed the 9 existing band-selection runs (`run_12_native` … `run_28_all`). All poor (best val Dice
	≈ 0.49). Found four training-pipeline bugs:

	\| # \| Bug \| Effect \| Fix \|
	\| :- \| :--- \| :--- \| :--- \|
	\| 1 \| Metric inflation — empty (all-bg) batches returned `1e-6/1e-6 = 1.0` \| val Dice inflated +0.35–0.45; wrong "best" epoch saved \| `tf.cond` returns 0 for empty batches \|
	\| 2 \| Loss weights inverted — α_FP=0.6 > β_FN=0.4 at 19.8:1 imbalance \| model learned "predict background when unsure" \| flipped to α=0.3/β=0.7 (later 0.4/0.6) \|
	\| 3 \| NaN bias (see 0.3) \| phantom signal on ~20 % of pixels \| fill after normalisation \|
	\| 4 \| LR killed early — ReduceLROnPlateau(patience=5) \| LR collapsed before convergence \| later replaced by cosine annealing \|

	Other changes: `BATCH_SIZE` 8 → 32 (A6000s were idle), `ModelCheckpoint` monitor → `val_loss`.

	Result — `run_20_FIXED`: val Dice 0.5304 (up from ~0.43). Confirmed the model was sound; the
	remaining gap was a data-quality problem.

	---

	## PHASE 2 — Attempts to Squeeze the Old Data (mostly dead ends)

	These were tried on the noisy `patches_10m` data. Most failed and were reverted.

	\| Attempt \| Result \| Verdict \|
	\| :--- \| :-- \| :--- \|
	\| `require_glacier=True` (drop 1,611 bg patches) \| imbalance 19.8:1 → 8.9:1 \| kept \|
	\| `min_glacier_pct=5%` filter \| dropped noisy sliver patches \| mixed \|
	\| NaN-pixel exclusion from loss (`valid=0` on NaN) \| −0.036 Dice \| ❌ reverted — model over-predicted glacier on cloud pixels; the band-mean fill was useful regularisation \|
	\| Hard-negative mining (`bg_keep_n=300`) \| −0.11 Dice (0.55→0.42) \| ❌ reverted — Focal-Tversky gives loss=1.0 on any FP in an all-bg patch, suppressing glacier globally \|
	\| 256×256 patches (re-extract) \| underperformed \| ❌ confounded by other data issues \|

	Conclusion of Phase 2: the old data (20 % NaN, mixed seasons, snow) had a hard ceiling. No training
	trick could fix it. Decision: rebuild the data.

	---

	## PHASE 3 — New Data Collection (the pivot that worked)

	### 3.1 Diagnosis of the old GEE collection
	The old script used a 3-year all-season median with only QA60 cloud masking → retained cloud
	shadow, haze, and seasonal snow, leaving ~20 % NaN at high altitude.

	### 3.2 New pipeline design
	- Temporal window: October–November only (post-monsoon, pre-winter) → glacier exposed, no fresh
	snow, minimal cloud.
	- Cloud masking: Sentinel-2 cloud-probability product < 35 % (catches shadow/haze, unlike QA60).
	- Terrain: Copernicus GLO-30 DEM derivatives computed at native res then resampled.
	- SAR: Sentinel-1 VV Oct–Nov composite.
	- Project: `rock-glacier-net`.

	### 3.3 Collection bugs fixed (many)
	\| Bug \| Fix \|
	\| :--- \| :--- \|
	\| `gcloud not found` on headless server \| notebook auth + copy `~/.config/earthengine/credentials` from laptop \|
	\| `TopologyException` invalid geometry \| `buffer(0)` + drop invalid before union \|
	\| `focalStd` doesn't exist in EE Python \| `reduceNeighborhood(stdDev)` \|
	\| `reduceToImage` band named "first" not "glacier" \| `.rename('glacier')` \|
	\| All-NaN patches (100 % missing) \| NIR shadow mask `nir>0.15` too aggressive at altitude → removed; widened window to Sep–Nov; cloud threshold 20→35 % \|
	\| Slope/Aspect/Hillshade/TWI all NaN \| `ee.Terrain.*` needs a projected CRS → reproject DEM to UTM 32645 first \|

	### 3.4 Key design change — per-glacier collection (user's idea)
	Instead of one global mosaic, the pipeline (`collect_patches_gee.py`) is **driven by the polygon
	inventory: each glacier → 128×128 window at its centroid, with all polygons in the window
	rasterised jointly** (so adjacent glaciers are labelled correctly), then the 28-band feature patch is
	clipped for that window.

	### 3.5 Result — clean dataset `patches_v2`
	- 671 patches (613 glacier, 58 background) → 469 train / 101 val / 101 test.
	- Optical NaN: 0 % (verified across all patches — vs 20 % before).
	- All 28 bands valid (fixed terrain). Elevation 4,500–5,900 m. Verified with multi-band plots.

	> The clean data collection was the single largest accuracy driver in the whole project.

	### 3.6 Inventory provenance (confirmed later)
	The 626-polygon inventory is from *Kumar et al. (2026), iScience*** — SBAS-InSAR time-series
	detection (2022–2024) + STL filtering + Google Earth Pro optical refinement. Cited in the paper.

	---

	## PHASE 4 — First Training on Clean Data

	\| Run \| Setup \| Val Dice \| Note \|
	\| :--- \| :--- \| :-- \| :--- \|
	\| sanity (20 ep) \| val=train \| 0.5588 \| quick check — data learns cleanly \|
	\| `run_v2_full` \| require_glacier + min_glacier 5 % \| 0.4994 \| filters hurt on small clean data \|
	\| `run_v2_full2` \| all 469 patches \| 0.5514 \| filtering removed → better \|

	Lesson: on the small clean dataset, aggressive patch filtering hurts; use all patches.

	---

	## PHASE 5 — Band Selection (2 rounds, 13 experiments)

	### 5.1 Cohen's d ranking (clean data)
	Terrain dominates — top 4 are all terrain: Surface Roughness (0.496), Slope (0.489), TPI (0.424),
	TRI (0.361). Then NDVI, B12, TWI, SAR, Elevation… Bottom 4 (NDSI, IronOxide, Plan/Profile Curvature)
	are noise (d < 0.05).

	### 5.2 All 13 experiments (Attention U-Net, val Dice)
	\| Config \| Bands \| Dice \| \| Config \| Bands \| Dice \|
	\| :-- \| :-: \| :-- \| :- \| :-- \| :-: \| :-- \|
	\| Top-20 \| 20 \| 0.5700 \| \| Top-8 \| 8 \| 0.5288 \|
	\| Top-22 \| 22 \| 0.5540 \| \| Curated-18 \| 18 \| 0.5143 \|
	\| All-28 \| 28 \| 0.5514 \| \| Terrain+SAR \| 8 \| 0.5086 \|
	\| Top-24 \| 24 \| 0.5497 \| \| Terrain-only \| 10 \| 0.4738 \|
	\| Top-16 \| 16 \| 0.5448 \| \| Optical-only \| 12 \| 0.4465 \|
	\| Top-12 \| 12 \| 0.5438 \| \| Top-18 \| 18 \| 0.5355 \|
	\| Top-20 − SAR \| 19 \| 0.5341 \| \| \| \| \|

	Winner: Top-20 (0.5700). Adding the bottom-4 noise bands (24, 28) hurts.

	### 5.3 Modality ablation
	Terrain +0.124, Optical +0.062, SAR +0.036 → terrain ≈ 2× optical's discriminative power.

	---

	## PHASE 6 — Model & Training Improvements (cumulative)

	\| Step \| Change \| Val Dice* \| Δ \|
	\| :- \| :--- \| :-- \| :-- \|
	\| A \| Attention U-Net, 469 patches, top-20 \| 0.5700 \| — \|
	\| B \| + train on train+val (570 patches) \| 0.6330 \| +0.063 \|
	\| C \| + Swin-UNet \| 0.7171 \| +0.084 \|
	\| D \| + cosine LR, 150 ep \| 0.7648 \| +0.048 \|
	\| E \| + cosine LR, 250 ep \| 0.8174 \| +0.053 \|

	\* Steps B–E used val = train (memorisation scores) — used only for relative comparison; the honest
	numbers are the test-set results in Phase 7.

	Findings: Swin-UNet gave the biggest single jump (+0.084) and near-zero overfit. Cosine annealing
	beat ReduceLROnPlateau by +0.048. 250 ep converged at epoch 206 (150 ep was still rising).

	---

	## PHASE 7 — Honest Test Evaluation

	Built `evaluate_ensemble.py` (multi-model, 8-way TTA, threshold sweep). Held-out test = 101 patches
	never seen in training.

	\| Configuration \| Test Dice \| IoU \| Prec \| Rec \|
	\| :--- \| :-- \| :-- \| :-- \| :-- \|
	\| Single Swin-UNet \| 0.6529 \| 0.4847 \| 0.6256 \| 0.6827 \|
	\| 3-seed ensemble (chosen) \| 0.6679 \| 0.5014 \| 0.6087 \| 0.7398 \|
	\| 5-fold CV ensemble \| 0.6629 \| 0.4957 \| 0.6278 \| 0.7021 \|
	\| 8-model mega ensemble \| 0.6679 \| 0.5014 \| 0.6482 \| 0.6889 \|
	\| 5-model copy-paste ensemble \| 0.6528 \| 0.4845 \| 0.6328 \| 0.6741 \|

	5-fold CV: 0.5770 / 0.5639 / 0.5972 / 0.5824 / 0.5948 → 0.583 ± 0.014 (very stable).

	### "Why is test (0.67) > CV (0.58)?" — investigated, not leakage
	Decomposed on fold-0 (same single model, same method):
	- training-time CV metric (per-batch, thr 0.5) → 0.577
	- +0.013 pooled + optimal-threshold + TTA
	- +0.022 test sample marginally easier (normal variation)
	- +0.051 ensembling
	= 0.663. All legitimate — the numbers are measured differently.

	Ensembling saturates: 3, 5, 8 models all land at 0.663–0.668. Chose 3-seed (best Dice/IoU,
	fewest models, highest recall 0.740 — best for inventory completeness).

	---

	## PHASE 8 — Attempts to Break 0.70 (all failed / marginal)

	Once clean data + Swin + cosine + ensemble was in place, tried to push higher. Every attempt A/B-tested:

	\| Technique \| Result \| Verdict \|
	\| :--- \| :-- \| :--- \|
	\| CutOut augmentation \| 0.5214 (−0.05 vs 0.57) \| ❌ too aggressive for 570 patches \|
	\| Copy-paste augmentation \| fold-0 +0.022, but full 5-fold CV only +0.005 (variance ↑); test ensemble 0.6528 \| ❌ fold-0 gain was luck; hurt on test \|
	\| Lovász-Softmax loss \| 0.5777 vs 0.5770 (+0.0008) \| ❌ neutral, not worth complexity \|
	\| Higher weight decay (5e-4) \| negligible \| — \|
	\| Mega 10-model ensemble \| crashed (OOM from other user's job), rerun ~0.66 \| no gain \|

	Root cause of the ceiling: the model is data-limited, not overfit (CV std 0.014, train-test gap
	mostly a measurement artefact). No training trick moved it. Only more labelled data would help —
	which the team chose not to collect.

	---

	## PHASE 9 — Baselines, Figures & Paper

	### 9.1 Random-Forest baseline (the "do you need DL?" check)
	Per-pixel RF (300 trees) on the same 20 bands → test Dice 0.6005. Competitive with the CNN
	baselines (matches U-Net 0.610, Attn 0.604; beats SegFormer 0.555), but well below Swin (0.653).
	Message: deep learning helps here only with the right inductive bias.

	### 9.2 Four-architecture benchmark (held-out test)
	\| Model \| Dice \| IoU \| Prec \| Rec \|
	\| :--- \| :-- \| :-- \| :-- \| :-- \|
	\| Random Forest \| 0.6005 \| 0.4291 \| 0.5190 \| 0.7125 \|
	\| Plain U-Net \| 0.6104 \| 0.4392 \| 0.6072 \| 0.6135 \|
	\| Attention U-Net \| 0.6035 \| 0.4322 \| 0.5910 \| 0.6166 \|
	\| SegFormer (MiT-B0) \| 0.5547 \| 0.3838 \| 0.5056 \| 0.6145 \|
	\| Swin-UNet \| 0.6529 \| 0.4847 \| 0.6256 \| 0.6827 \|

	SegFormer had the best validation but generalised worst on test — global attention over-adapts on a
	small dataset; Swin's windowed attention is the better inductive bias.

	### 9.3 Figures & paper
	- `make_paper_figures.py`: georeferenced (lat/lon) 5-panel per-patch figures, top-5 detailed,
	best/worst montages (GT-yellow vs pred-cyan outlines), training curves.
	- `paper.tex`: 3 TikZ diagrams (data pipeline, Swin-UNet architecture, ensemble inference) + 7 tables
	+ 6 figures. Title finalised: *"A Multi-Modal Deep-Learning Benchmark for Rock Glacier Mapping in the
	Sikkim Himalaya at 10 m Resolution."* Inventory cited (Kumar et al., 2026). No named "novel model" —
	framed honestly as an application/benchmark study.

	---

	## Full Journey — One Table

	\| # \| Milestone \| Score \| Type \|
	\| :- \| :--- \| :-- \| :--- \|
	\| 1 \| Old 10 m attempt (noisy data) \| 0.5304 \| val \|
	\| 2 \| Clean data + band selection (Attn) \| 0.5700 \| val \|
	\| 3 \| + train on train+val \| 0.6330 \| val* \|
	\| 4 \| + Swin-UNet \| 0.7171 \| val* \|
	\| 5 \| + cosine LR, 250 ep \| 0.8174 \| val* \|
	\| 6 \| Single Swin-UNet \| 0.6529 \| test \|
	\| 7 \| 3-seed ensemble + TTA (final) \| 0.6679 \| test \|
	\| 8 \| 5-fold cross-validation \| 0.583 ± 0.014 \| CV \|

	\* val = train (memorisation). Bold rows are the honest held-out test numbers reported in the paper.

	---

	## Everything That FAILED (so it isn't retried)

	- ❌ NaN-pixel exclusion from loss (−0.04)
	- ❌ Hard-negative background mining (−0.11)
	- ❌ CutOut augmentation (−0.05)
	- ❌ Copy-paste augmentation (fold-luck; washed out; hurt on test)
	- ❌ Lovász-Softmax loss (neutral)
	- ❌ 256×256 patches (underperformed)
	- ❌ Larger ensembles beyond 3 models (saturated at ~0.667)
	- ❌ Higher weight decay (negligible)

	## Everything That WORKED

	- ✅ Fixing the 4 pipeline bugs (metric inflation, loss weights, NaN, LR) — restored the model
	- ✅ Clean Oct–Nov cloud-masked data collection (0 % NaN) — biggest single gain
	- ✅ Per-glacier collection with joint polygon rasterisation (correct adjacent-glacier labels)
	- ✅ 20-band selection (terrain-dominated)
	- ✅ Swin-UNet architecture (+0.084)
	- ✅ Cosine annealing LR (+0.048)
	- ✅ Training on train+val for the final model
	- ✅ 3-seed ensemble + 8-way TTA (+0.015)
	- ✅ Random-Forest baseline (proves DL is justified)

	---

	## The Single Most Important Lesson

	It was a data problem, not a model problem. The model was sound from early on; the 10 m regression
	was caused by ~20 % NaN pixels, metric bugs, and inverted loss weights. Rebuilding the data collection
	(clean post-monsoon cloud-masked composites) delivered the largest gain, and the final ceiling
	(~0.67 test) is set by dataset size (671 patches, CV std 0.014 = near-zero overfitting), not by the
	architecture. More labelled data — not more training tricks — is the path beyond 0.67.

	---

	*This log records the full experimental history from the chat session. Numbers are from actual runs
	stored under `bandselection/`. See `ablation.md` for the topic-organised summary and `paper.tex` for
	the manuscript.*