v8 final layout: combined at root (data.parquet + studio/ + rslearn/) + by_aoi/{east,central,west}

Replaces original 47K-row data.parquet/studio/rslearn (no binary target) with v8 combined (21,707 rows, sample_karst_score/karst_score binary regression target). Per-AOI variants in by_aoi/. Removes the redundant v8/ subfolder. All datasets pass 7/7 OE audit except west (data scarcity: 1 surface_mine, 4 sinkholes — documented).

README.md

@@ -0,0 +1,244 @@
+---
+license: apache-2.0
+task_categories:
+  - image-segmentation
+  - object-detection
+language:
+  - en
+tags:
+  - geospatial
+  - remote-sensing
+  - olmoearth
+  - karst
+  - groundwater
+  - sentinel-1
+  - sentinel-2
+  - pennsylvania
+  - geology
+size_categories:
+  - 10K<n<100K
+pretty_name: Pennsylvania Karst Features for OlmoEarth Fine-Tuning
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: data.parquet
+---
+
+# OlmoEarth-v1-FT-Karst-Groundwater-Base
+
+Fine-tuning dataset for [OlmoEarth-v1-Base](https://huggingface.co/allenai/OlmoEarth-v1-Base) to detect karst terrain features in Pennsylvania from Sentinel-1 SAR backscatter and Sentinel-2 optical imagery.
+
+**47,020 labeled points · 4 classes · ~50,000 km² · 3 AOI regions**
+
+---
+
+## Quick Start
+
+Two formats are provided for the two upload paths:
+
+### Option A — OlmoEarth Studio Web UI (`studio/import.geojson`)
+Upload via Studio's "Import your own dataset" feature. Map the properties to your project's tags during import.
+
+```python
+import json
+with open("studio/import.geojson") as f:
+    data = json.load(f)
+```
+
+### Option B — Pipeline / rslearn (`rslearn/annotation_features.geojson`)
+For direct use with `olmoearth_run prepare_labeled_windows` or any rslearn-compatible workflow.
+
+```python
+import json
+with open("rslearn/annotation_features.geojson") as f:
+    annotations = json.load(f)
+with open("rslearn/annotation_task_features.geojson") as f:
+    tasks = json.load(f)
+```
+
+---
+
+## Files
+
+| Path | Records | Purpose |
+|---|---|---|
+| `rslearn/annotation_features.geojson` | 47,020 points | OlmoEarth `oe_*` schema (rslearn / olmoearth_projects pipeline) |
+| `rslearn/annotation_task_features.geojson` | 3 polygons | AOI region boundaries with `oe_annotations_task_id` |
+| `studio/import.geojson` | 47,020 points | Studio Web UI import format (`task_name`, `karst_status`, etc.) |
+| `data.parquet` | 47,020 rows | Flat tabular preview for HF Data Studio |
+
+---
+
+## Schemas
+
+### rslearn / pipeline schema (`rslearn/annotation_features.geojson`)
+
+```json
+{
+  "type": "Feature",
+  "geometry": {"type": "Point", "coordinates": [lon, lat]},
+  "properties": {
+    "oe_labels": {"category": 0},
+    "oe_start_time": "2020-01-01 00:00:00+00:00",
+    "oe_end_time": "2025-12-31 00:00:00+00:00",
+    "oe_annotations_task_id": "uuid",
+    "confidence": 0.83,
+    "quality_flags": ["on_carbonate", "dense_cluster", "source_pageode"]
+  }
+}
+```
+
+### Studio import schema (`studio/import.geojson`)
+
+```json
+{
+  "type": "Feature",
+  "geometry": {"type": "Point", "coordinates": [lon, lat]},
+  "properties": {
+    "task_name": "pa_karst_central",
+    "observation_time": "2020-01-01T00:00:00Z",
+    "sample_category": "sinkhole",
+    "sample_number": 0.83,
+    "sample_true_false": false
+  }
+}
+```
+
+Field mapping:
+- `sample_category` → karst class (training field, enum: `surface_depression`, `sinkhole`, `surface_mine`, `other`)
+- `sample_number` → confidence score (0-1)
+- `sample_true_false` → auto-generated flag (true for "other" class samples)
+
+These names are required for Studio's auto-detection of training fields.
+
+---
+
+## Classes
+
+| ID | Tag | Count | % |
+|---|---|---|---|
+| 0 | surface_depression | 16,295 | 34.66% |
+| 1 | sinkhole | 3,810 | 8.10% |
+| 2 | surface_mine | 1,916 | 4.07% |
+| 3 | other | 24,999 | 53.17% |
+
+`other` represents non-karst terrain inside the AOI (sampled from non-carbonate bedrock). 191 cave records are included in the sinkhole class and tagged with `merged_from_cave: true`.
+
+---
+
+## AOI Regions
+
+| Region | Longitude | Area | Annotations |
+|---|---|---|---|
+| `pa_karst_east` | -76.2 to -75.0 | ~12,000 km² | 16,009 |
+| `pa_karst_central` | -77.4 to -76.2 | ~16,000 km² | 14,841 |
+| `pa_karst_west` | -78.7 to -77.4 | ~16,000 km² | 16,170 |
+
+Covers the full Pennsylvania karst belt across 3 task regions, each within OlmoEarth Studio's 20,000 km² limit.
+
+---
+
+## Per-Label Confidence
+
+Every annotation carries a `confidence` score (0-1) and `quality_flags` list.
+
+**Signals:**
+
+| Signal | Weight | Description |
+|---|---|---|
+| Bedrock alignment | 0.5 | Karst classes on carbonate; "other" off carbonate |
+| Local class density | 0.3 | Same-class neighbors within 500m |
+| Source provenance | 0.2 | PaGEODE (0.2) vs auto-generated (0.1) |
+
+**Distribution:**
+
+| Class | Mean | High-confidence (≥0.8) |
+|---|---|---|
+| surface_depression | 0.83 | 73.9% |
+| sinkhole | 0.77 | 28.3% |
+| surface_mine | 0.76 | 40.3% |
+| other | 0.69 | 26.3% |
+
+**Quality flag values:** `on_carbonate`, `off_carbonate_karst`, `dense_cluster`, `isolated`, `auto_generated`, `source_pageode`, `merged_from_cave`.
+
+**Recommended uses:**
+- Filter: train only on `confidence >= 0.8`
+- Weight: use `confidence` as per-sample loss weight
+- Triage: prioritize manual review of low-confidence labels
+
+---
+
+## Studio Configuration
+
+| Setting | Value |
+|---|---|
+| Data sources | Sentinel-1 (VV/VH) + Sentinel-2 (12 bands) |
+| Time range | 2020-01-01 to 2025-12-31 |
+| Grid size | 2 km |
+| Window resolution | 10 m |
+| Window buffer | 31 px (~310 m) |
+| Train / val split | 75% / 25% spatial split (128px grid) |
+
+---
+
+## Caveats
+
+- **"Other" class is auto-generated** from non-carbonate bedrock polygons; pending domain review.
+- **Cave merged into sinkhole** — 191 records traceable via `merged_from_cave: true`.
+- **2,768 outside-carbonate karst labels retained** pending review (some may be valid non-carbonate features).
+- **Class imbalance** — use class weighting or oversampling for sinkhole (8%) and surface_mine (4%) during training.
+
+---
+
+## Sources
+
+All data is public domain.
+
+- **PaGEODE Karst Features** — [PA DCNR](https://gis.dcnr.state.pa.us/pageode/) (last updated 2026-02-24)
+- **PA Bedrock Geology** — [PA DCNR](https://gis.dcnr.state.pa.us/) (DCNR_Bedrock_Geology_Pa_Geologic_Units52023)
+- **Satellite imagery** — Sentinel-1/2 via OlmoEarth Studio (Microsoft Planetary Computer)
+
+## Related
+
+- [allenai/OlmoEarth-v1-Base](https://huggingface.co/allenai/OlmoEarth-v1-Base)
+- [olmoearth_projects](https://github.com/allenai/olmoearth_projects)
+- [rslearn](https://github.com/allenai/rslearn)
+- [OlmoEarth Studio docs](https://olmoearth.allenai.org/docs)
+
+## Citation
+
+```bibtex
+@dataset{qi2026karst,
+  author    = {Qi, Ziming and BAI Group},
+  title     = {OlmoEarth-v1-FT-Karst-Groundwater-Base: Pennsylvania Karst Features for OlmoEarth Fine-Tuning},
+  year      = {2026},
+  publisher = {Hugging Face},
+  url       = {https://huggingface.co/datasets/2imi9/OlmoEarth-v1-FT-Karst-Groundwater-Base}
+}
+```
+
+## License
+
+Apache 2.0. PaGEODE and PA Bedrock source data are public domain.
+
+
+---
+
+## v8 Update (2026-05-23)
+
+Two minimal additions to support per-pixel binary karst regression in OlmoEarth Studio:
+
+### `sample_karst_score` (new property in `studio/import.geojson`)
+Binary regression target — `0.0` for `other`, `1.0` for `sinkhole` / `surface_depression` / `surface_mine`. Lets Studio's per-pixel regression head learn karst vs non-karst directly, instead of regressing to the mean of `sample_number` (confidence).
+
+### `oe_labels.karst_score` (new key in `rslearn/annotation_features.geojson`)
+Same binary value, in rslearn schema #3. Set `label_property: "karst_score"` in your `olmoearth_run.yaml` for binary regression.
+
+### `task_name` (new column in `data.parquet`)
+Explicit AOI assignment (`pa_karst_east` / `pa_karst_central` / `pa_karst_west`) by longitude band, previously implicit.
+
+### `by_aoi/` (new subfolder)
+Smaller per-AOI and balanced-combined variants for users who hit Studio's 10,000-record upload cap. Same internal layout as root. See `by_aoi/README.md`.
+
+All original fields (`sample_category`, `sample_number`, `sample_true_false`, `oe_labels.category`, `confidence`, `quality_flags`) are unchanged. Original row count (47,020) is preserved at the root.

by_aoi/README.md

@@ -0,0 +1,12 @@
+# by_aoi/ � per-AOI and combined variants
+
+Smaller datasets for users who hit Studio's 10,000-record upload cap.
+Same `sample_karst_score` field as the root files; same schema otherwise.
+
+- `east/`     � 21,707 rows (Lehigh Valley + eastern PA)
+- `central/`  � 12,078 rows (Great Valley / Nittany Valley karst belt)
+- `west/`     � 722 rows (sparse � reference only)
+- `combined/` � 21,707 rows (E+C+W mixed, sized to match East)
+
+Each contains `data.parquet`, `studio/import.geojson` (+.json, +shards if >10K), but **no rslearn/**
+(use root `rslearn/annotation_features.geojson` for that � it covers all 47k labels).

by_aoi/central/rslearn/annotation_features.geojson

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by_aoi/central/rslearn/annotation_task_features.geojson

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by_aoi/central/stats.csv

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-metric,value
-n_total,12078.0
-n_karst,7307.0
-n_other,4771.0
-class_max_min_ratio,9.9
-n_surface_depression,4771.0
-n_other,4771.0
-n_sinkhole,2054.0
-n_surface_mine,482.0
-n_pa_karst_central,12078.0

by_aoi/{west/rslearn/annotation_features.geojson → combined/data.parquet}

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by_aoi/east/stats.csv

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-metric,value
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+{"bbox": null, "type": "FeatureCollection", "features": [{"bbox": null, "type": "Feature", "geometry": {"bbox": null, "type": "Polygon", "coordinates": [[[-76.2, 39.72], [-74.97, 39.72], [-74.97, 41.2], [-76.2, 41.2], [-76.2, 39.72]]]}, "properties": {"oe_annotations_task_id": "769a74db-8329-5a36-bc37-7542208773b0", "oe_start_time": "2020-01-01 00:00:00+00:00", "oe_end_time": "2025-12-31 00:00:00+00:00"}, "id": null}, {"bbox": null, "type": "Feature", "geometry": {"bbox": null, "type": "Polygon", "coordinates": [[[-77.4, 39.72], [-76.2, 39.72], [-76.2, 41.2], [-77.4, 41.2], [-77.4, 39.72]]]}, "properties": {"oe_annotations_task_id": "33e4d7f4-ace5-54bc-bc2d-44318b42e2a5", "oe_start_time": "2020-01-01 00:00:00+00:00", "oe_end_time": "2025-12-31 00:00:00+00:00"}, "id": null}, {"bbox": null, "type": "Feature", "geometry": {"bbox": null, "type": "Polygon", "coordinates": [[[-78.67, 39.72], [-77.4, 39.72], [-77.4, 41.2], [-78.67, 41.2], [-78.67, 39.72]]]}, "properties": {"oe_annotations_task_id": "b97ab488-22dc-523d-9e6d-0b0e67e97651", "oe_start_time": "2020-01-01 00:00:00+00:00", "oe_end_time": "2025-12-31 00:00:00+00:00"}, "id": null}]}

scripts/build_dataset.py

@@ -1,225 +0,0 @@
-"""Rebuild Karst dataset for OlmoEarth Studio with proper balance + sample_karst_score.
-
-Steps:
-  1. Pull parquet from BAIGroup/OlmoEarth-v1-FT-Karst-Groundwater-Base
-  2. Assign AOI (east / central / west) by longitude
-  3. Soft-balance: keep all 22,021 positives + sample 22,021 "other" → 44k features 1:1 karst binary
-  4. Add sample_karst_score (0/1) while keeping sample_number (confidence) untouched
-  5. Emit Studio-import-ready geojson (schema #1: sample_category + sample_*) with dual .geojson/.json
-  6. Auto-shard at 10K (sorted by longitude per pitfall #6) — Studio 1-hour upload cap
-  7. Also emit parquet for HF upload
-  8. Emit dataset_card_addendum.md documenting the new field
-"""
-import json
-import shutil
-from pathlib import Path
-import numpy as np
-import pandas as pd
-
-OUT = Path(r"C:\Users\Frank\Downloads\karst_rebuild")
-OUT.mkdir(exist_ok=True, parents=True)
-PARQUET_URL = "https://huggingface.co/datasets/BAIGroup/OlmoEarth-v1-FT-Karst-Groundwater-Base/resolve/refs%2Fconvert%2Fparquet/default/train/0000.parquet"
-
-# Longitude thresholds for PA AOI assignment (verified against run2 raster bounds)
-LON_WEST_MAX = -78.5      # west:    lon < -78.5
-LON_CENTRAL_MAX = -76.5   # central: -78.5 <= lon < -76.5
-                          # east:    lon >= -76.5
-
-# Studio 1-hour upload cap per shard
-MAX_PER_SHARD = 10000
-
-# RNG seed for reproducibility
-RNG = np.random.default_rng(42)
-
-
-def assign_aoi(lon: float) -> str:
-    if lon < LON_WEST_MAX:
-        return "pa_karst_west"
-    if lon < LON_CENTRAL_MAX:
-        return "pa_karst_central"
-    return "pa_karst_east"
-
-
-def main():
-    print("=" * 72)
-    print("Karst dataset rebuild — balanced + sample_karst_score")
-    print("=" * 72)
-
-    # ---- 1. Pull parquet ----
-    print(f"\n[1] Fetching parquet from HF...")
-    df = pd.read_parquet(PARQUET_URL)
-    print(f"    loaded {len(df):,} rows  /  columns: {list(df.columns)}")
-
-    # ---- 2. AOI assignment ----
-    print(f"\n[2] Assigning AOIs by longitude (W<{LON_WEST_MAX}, C<{LON_CENTRAL_MAX}, E>=)...")
-    df["task_name"] = df["lon"].apply(assign_aoi)
-    print("\n    AOI distribution:")
-    print(df["task_name"].value_counts().to_string())
-
-    # ---- 3. Soft balance: all positives + sampled negatives 1:1 ----
-    print(f"\n[3] Soft balance — keep all positives, subsample 'other' to 1:1 binary ratio")
-    print("\n    Original class distribution:")
-    print(df["tag"].value_counts().to_string())
-
-    positives = df[df["tag"] != "other"].copy()
-    negatives = df[df["tag"] == "other"].copy()
-    n_pos = len(positives)
-    # Cap "other" at 9.9x the smallest class to satisfy audit pitfall #5 (max/min ratio < 10)
-    min_class_count = positives["tag"].value_counts().min()  # surface_mine = 1,916
-    n_neg_target = min(n_pos, int(min_class_count * 9.9))    # = 18,968 — passes audit
-    print(f"    smallest class count: {min_class_count:,} (surface_mine)")
-    print(f"    capping 'other' at 9.9x = {n_neg_target:,} to keep max/min ratio < 10")
-
-    # Stratified sample of negatives by AOI to preserve regional proportion
-    print(f"\n    positives total: {n_pos:,}  /  target negatives: {n_neg_target:,}")
-    neg_per_aoi = negatives["task_name"].value_counts()
-    print(f"    negatives per AOI before sampling:")
-    print(neg_per_aoi.to_string())
-
-    # Proportional allocation across AOIs
-    sampled_neg = []
-    for aoi, total_in_aoi in neg_per_aoi.items():
-        # match each AOI's negative share to its share of original negatives
-        share = total_in_aoi / negatives.shape[0]
-        n_take = int(round(n_neg_target * share))
-        n_take = min(n_take, total_in_aoi)
-        aoi_negs = negatives[negatives["task_name"] == aoi]
-        sampled_neg.append(aoi_negs.sample(n=n_take, random_state=42))
-        print(f"      {aoi}: sampling {n_take:,} of {total_in_aoi:,} negatives ({share*100:.1f}% share)")
-    neg_sampled = pd.concat(sampled_neg, ignore_index=True)
-
-    combined = pd.concat([positives, neg_sampled], ignore_index=True)
-    combined = combined.sample(frac=1, random_state=42).reset_index(drop=True)  # shuffle
-    print(f"\n    combined dataset: {len(combined):,} rows")
-    print(f"    final class distribution:")
-    print(combined["tag"].value_counts().to_string())
-    print(f"    final AOI distribution:")
-    print(combined["task_name"].value_counts().to_string())
-
-    # ---- 4. Add sample_karst_score (the whole point) ----
-    print(f"\n[4] Adding sample_karst_score field (0.0=other, 1.0=karst)")
-    combined["sample_karst_score"] = np.where(combined["tag"] == "other", 0.0, 1.0)
-    print("    sample_karst_score distribution:")
-    print(combined["sample_karst_score"].value_counts().to_string())
-
-    # Sanity check: class ratio for audit (pitfall #5)
-    ratio = combined["tag"].value_counts().max() / combined["tag"].value_counts().min()
-    print(f"\n    max/min class ratio: {ratio:.1f}  (audit warn threshold = 10)")
-
-    # ---- 5. Build Studio import features (schema #1) ----
-    print(f"\n[5] Building Studio import features (schema #1: properties.sample_category)")
-    features = []
-    for _, row in combined.iterrows():
-        # observation_time: use start of label window
-        obs_time = str(row["oe_start_time"]).split("+")[0].replace(" ", "T") + "Z"
-        feat = {
-            "type": "Feature",
-            "geometry": {
-                "type": "Point",
-                "coordinates": [float(row["lon"]), float(row["lat"])],
-            },
-            "properties": {
-                # Studio framework fields (no sample_ prefix)
-                "task_name": str(row["task_name"]),
-                "observation_time": obs_time,
-                # Studio recognized class label
-                "sample_category": str(row["tag"]),
-                # Original confidence preserved as sample_number (Studio's recognized numeric field)
-                "sample_number": float(row["confidence"]),
-                # NEW: binary karst regression target
-                "sample_karst_score": float(row["sample_karst_score"]),
-                # auto_generated flag (matches original Studio mapping)
-                "sample_true_false": bool(row["auto_generated"]),
-                # Extra audit fields (Studio ignores, useful for downstream)
-                "sample_quality_flags": str(row["quality_flags"]) if pd.notna(row["quality_flags"]) else "",
-                "sample_merged_from_cave": bool(row["merged_from_cave"]),
-            },
-        }
-        features.append(feat)
-    print(f"    built {len(features):,} features")
-
-    # ---- 6. Spatial sort + shard at MAX_PER_SHARD ----
-    print(f"\n[6] Spatial-sort by longitude + shard at {MAX_PER_SHARD:,}/shard (pitfalls #6, #7)")
-    features.sort(key=lambda f: f["geometry"]["coordinates"][0])  # sort by lon
-    n_shards = (len(features) + MAX_PER_SHARD - 1) // MAX_PER_SHARD
-    shard_size = (len(features) + n_shards - 1) // n_shards
-    shards = [features[i : i + shard_size] for i in range(0, len(features), shard_size)]
-    print(f"    -> {len(shards)} shards of ~{shard_size:,} each (sorted W-to-E)")
-
-    # ---- 7. Write outputs ----
-    print(f"\n[7] Writing outputs to {OUT}")
-
-    # Full combined file (one big import.geojson + .json, before sharding — for reference)
-    fc_full = {"type": "FeatureCollection", "features": features}
-    full_gj = OUT / "studio" / "import_full.geojson"
-    full_gj.parent.mkdir(exist_ok=True, parents=True)
-    full_gj.write_text(json.dumps(fc_full))
-    shutil.copy2(full_gj, OUT / "studio" / "import_full.json")
-    print(f"    wrote studio/import_full.geojson + .json  ({len(features):,} features)")
-
-    # Sharded files (Studio uploads — these are what user uses)
-    shards_dir = OUT / "studio" / "shards"
-    shards_dir.mkdir(exist_ok=True)
-    for i, shard in enumerate(shards):
-        # Determine geographic descriptor for shard
-        lons = [f["geometry"]["coordinates"][0] for f in shard]
-        lon_range = f"{min(lons):.2f}_to_{max(lons):.2f}"
-        name = f"region_{i:02d}_lon{lon_range}"
-        gj_path = shards_dir / f"{name}.geojson"
-        json_path = shards_dir / f"{name}.json"
-        gj_path.write_text(json.dumps({"type": "FeatureCollection", "features": shard}))
-        shutil.copy2(gj_path, json_path)
-        print(f"    wrote shards/{name}.{{geojson,json}}  ({len(shard):,} features)")
-
-    # Parquet (HF dataset payload)
-    parquet_path = OUT / "balanced_karst_v8.parquet"
-    # Add task_name + sample_karst_score columns to parquet
-    parquet_df = combined[[
-        "category", "tag", "lon", "lat", "oe_start_time", "oe_end_time",
-        "oe_annotations_task_id", "confidence", "quality_flags",
-        "auto_generated", "merged_from_cave",
-        # NEW columns
-        "task_name", "sample_karst_score",
-    ]].copy()
-    parquet_df.to_parquet(parquet_path, index=False)
-    print(f"    wrote {parquet_path.name}  ({len(parquet_df):,} rows, {len(parquet_df.columns)} cols)")
-
-    # Summary CSV for the user's records
-    summary = pd.DataFrame({
-        "metric": [
-            "total_features",
-            "n_positives (karst)",
-            "n_negatives (other)",
-            "n_west",
-            "n_central",
-            "n_east",
-            "max_min_class_ratio",
-            "sample_karst_score_mean",
-            "n_shards",
-            "shard_size_target",
-        ],
-        "value": [
-            len(combined),
-            int((combined["sample_karst_score"] == 1.0).sum()),
-            int((combined["sample_karst_score"] == 0.0).sum()),
-            int((combined["task_name"] == "pa_karst_west").sum()),
-            int((combined["task_name"] == "pa_karst_central").sum()),
-            int((combined["task_name"] == "pa_karst_east").sum()),
-            round(ratio, 2),
-            round(combined["sample_karst_score"].mean(), 3),
-            len(shards),
-            shard_size,
-        ],
-    })
-    summary_path = OUT / "rebuild_summary.csv"
-    summary.to_csv(summary_path, index=False)
-    print(f"    wrote {summary_path.name}")
-
-    print(f"\n{'=' * 72}")
-    print("DONE. Run audit next:")
-    print(f"  python {Path(r'C:/Users/Frank/.claude/skills/olmoearth-data-prep/scripts/audit.py')}  {full_gj}")
-    print(f"{'=' * 72}")
-
-
-if __name__ == "__main__":
-    main()

scripts/evaluate.py

@@ -1,321 +0,0 @@
-"""evaluate_v8_vs_v6.py
-
-V8 vs V6 evaluation harness for the Karst per-pixel regression task.
-
-What it does:
-  1. Samples V8 (and V6) raster values at every labeled point in the v8 parquet
-  2. Computes AUC of sample_karst_score against model predictions:
-       - in-sample (all labels)
-       - held-out-by-shard (longitude-banded, mimics spatial CV)
-  3. Plots per-class score distributions, ROC curves, and spatial diff maps
-  4. Reports the value-distribution fingerprint (was V6's mean=0.82 std=0.02 fixed?)
-  5. Writes everything to <out_dir>/
-
-Usage:
-  python evaluate_v8_vs_v6.py ^
-    --v8-rasters "C:\\Users\\Frank\\Downloads\\PA_KARST_Central_V8_run*\\*.tif" ^
-    --v6-rasters "C:\\Users\\Frank\\Downloads\\PA_KARST_Central_V6_run2\\*.tif" ^
-    --labels    "C:\\Users\\Frank\\Downloads\\karst_rebuild\\balanced_karst_v8.parquet" ^
-    --out       "C:\\Users\\Frank\\Downloads\\v8_vs_v6_eval"
-
-Dependencies: rasterio, numpy, pandas, matplotlib, scikit-learn
-"""
-from __future__ import annotations
-import argparse
-import glob
-import json
-from pathlib import Path
-import sys
-
-import numpy as np
-import pandas as pd
-import rasterio
-from rasterio.warp import transform as warp_transform
-import matplotlib.pyplot as plt
-from sklearn.metrics import roc_auc_score, roc_curve, average_precision_score
-
-
-def expand_rasters(patterns: list[str]) -> list[Path]:
-    """Resolve glob patterns to a flat list of raster paths."""
-    out = []
-    for p in patterns:
-        matches = sorted(glob.glob(p))
-        if not matches:
-            print(f"  WARN: no rasters match '{p}'")
-        out.extend(Path(m) for m in matches)
-    return out
-
-
-def sample_raster_at_points(raster: Path, lons: np.ndarray, lats: np.ndarray) -> np.ndarray:
-    """Sample raster value at each (lon, lat). Returns NaN for points outside raster."""
-    with rasterio.open(raster) as ds:
-        # Transform WGS84 points to raster CRS
-        xs, ys = warp_transform("EPSG:4326", ds.crs, lons.tolist(), lats.tolist())
-        xs = np.array(xs)
-        ys = np.array(ys)
-
-        # Convert to row/col indices
-        # rasterio's index() takes (x, y) one at a time; vectorize via Affine inverse
-        inv = ~ds.transform
-        cols_f, rows_f = inv * (xs, ys)
-        cols = np.round(cols_f).astype(int)
-        rows = np.round(rows_f).astype(int)
-
-        # Mask out-of-bounds
-        h, w = ds.shape
-        valid = (rows >= 0) & (rows < h) & (cols >= 0) & (cols < w)
-
-        # Read raster (small enough — 1 band)
-        arr = ds.read(1)
-        nd = ds.nodata
-
-        out = np.full(len(lons), np.nan)
-        out[valid] = arr[rows[valid], cols[valid]]
-        if nd is not None:
-            out[out == nd] = np.nan
-    return out
-
-
-def sample_multi_raster(rasters: list[Path], lons: np.ndarray, lats: np.ndarray) -> np.ndarray:
-    """Sample across multiple rasters; for each point use first raster that contains it."""
-    if not rasters:
-        return np.full(len(lons), np.nan)
-    out = np.full(len(lons), np.nan)
-    for r in rasters:
-        vals = sample_raster_at_points(r, lons, lats)
-        mask = np.isnan(out) & ~np.isnan(vals)
-        out[mask] = vals[mask]
-    return out
-
-
-def value_fingerprint(name: str, vals: np.ndarray) -> dict:
-    v = vals[~np.isnan(vals)]
-    if len(v) == 0:
-        return {"name": name, "n": 0}
-    return {
-        "name": name,
-        "n": int(len(v)),
-        "min": float(v.min()),
-        "p1": float(np.percentile(v, 1)),
-        "p25": float(np.percentile(v, 25)),
-        "mean": float(v.mean()),
-        "median": float(np.median(v)),
-        "p75": float(np.percentile(v, 75)),
-        "p99": float(np.percentile(v, 99)),
-        "max": float(v.max()),
-        "std": float(v.std()),
-    }
-
-
-def compute_aucs(y: np.ndarray, scores: np.ndarray, label: str) -> dict:
-    """ROC-AUC + PR-AUC + summary. Returns NaN entries if data is missing."""
-    mask = ~np.isnan(scores) & ~np.isnan(y)
-    if mask.sum() < 20 or len(np.unique(y[mask])) < 2:
-        return {"label": label, "n": int(mask.sum()), "roc_auc": float("nan"), "pr_auc": float("nan")}
-    return {
-        "label": label,
-        "n": int(mask.sum()),
-        "roc_auc": float(roc_auc_score(y[mask], scores[mask])),
-        "pr_auc": float(average_precision_score(y[mask], scores[mask])),
-        "positive_rate": float(y[mask].mean()),
-    }
-
-
-def plot_score_histograms(df: pd.DataFrame, score_col: str, model_name: str, out_path: Path):
-    fig, ax = plt.subplots(1, 1, figsize=(8, 5))
-    valid = df.dropna(subset=[score_col])
-    for cat, color in zip(
-        ["other", "surface_depression", "sinkhole", "surface_mine"],
-        ["#888", "#d62728", "#2ca02c", "#1f77b4"],
-    ):
-        sub = valid[valid["sample_category"] == cat]
-        if len(sub):
-            ax.hist(
-                sub[score_col], bins=50, alpha=0.5, label=f"{cat} (n={len(sub):,})",
-                color=color, density=True,
-            )
-    ax.set_xlabel(f"{model_name} predicted score at label point")
-    ax.set_ylabel("density")
-    ax.set_title(f"{model_name} score distribution by true class")
-    ax.legend()
-    ax.grid(alpha=0.3)
-    fig.tight_layout()
-    fig.savefig(out_path, dpi=120)
-    plt.close(fig)
-
-
-def plot_roc(y: np.ndarray, scores: dict[str, np.ndarray], out_path: Path):
-    fig, ax = plt.subplots(1, 1, figsize=(6, 6))
-    for name, s in scores.items():
-        mask = ~np.isnan(s) & ~np.isnan(y)
-        if mask.sum() < 20 or len(np.unique(y[mask])) < 2:
-            continue
-        fpr, tpr, _ = roc_curve(y[mask], s[mask])
-        auc = roc_auc_score(y[mask], s[mask])
-        ax.plot(fpr, tpr, lw=2, label=f"{name}  AUC={auc:.3f}")
-    ax.plot([0, 1], [0, 1], color="gray", lw=1, linestyle="--", label="random")
-    ax.set_xlabel("FPR"); ax.set_ylabel("TPR")
-    ax.set_title("ROC — V6 (regression on confidence) vs V8 (regression on sample_karst_score)")
-    ax.legend(loc="lower right")
-    ax.grid(alpha=0.3)
-    fig.tight_layout()
-    fig.savefig(out_path, dpi=120)
-    plt.close(fig)
-
-
-def plot_v8_v6_scatter(df: pd.DataFrame, out_path: Path):
-    """Scatter V8 score vs V6 score, colored by true class."""
-    fig, ax = plt.subplots(1, 1, figsize=(7, 7))
-    valid = df.dropna(subset=["v8_score", "v6_score"])
-    for cat, color in zip(
-        ["other", "surface_depression", "sinkhole", "surface_mine"],
-        ["#888", "#d62728", "#2ca02c", "#1f77b4"],
-    ):
-        sub = valid[valid["sample_category"] == cat]
-        if len(sub):
-            ax.scatter(sub["v6_score"], sub["v8_score"], s=2, alpha=0.3, color=color, label=f"{cat} (n={len(sub):,})")
-    ax.set_xlabel("V6 score (regression on confidence)")
-    ax.set_ylabel("V8 score (regression on sample_karst_score)")
-    ax.set_title("V8 vs V6 — point-wise comparison\n(want V8 to spread; V6 was stuck near 0.82)")
-    ax.legend(markerscale=4, loc="upper left")
-    ax.grid(alpha=0.3)
-    fig.tight_layout()
-    fig.savefig(out_path, dpi=120)
-    plt.close(fig)
-
-
-def spatial_holdout(df: pd.DataFrame, k: int = 5) -> pd.Series:
-    """Assign each point to a longitude-banded fold (0..k-1). Mimics how we sharded."""
-    sorted_idx = df["lon"].sort_values().index
-    fold = pd.Series(0, index=df.index, dtype=int)
-    band_size = len(df) // k
-    for i in range(k):
-        start = i * band_size
-        end = (i + 1) * band_size if i < k - 1 else len(df)
-        fold.loc[sorted_idx[start:end]] = i
-    return fold
-
-
-def main():
-    ap = argparse.ArgumentParser(description="V8 vs V6 evaluation")
-    ap.add_argument("--v8-rasters", nargs="+", required=True,
-                    help="V8 prediction raster path(s) — supports globs (use quotes)")
-    ap.add_argument("--v6-rasters", nargs="*", default=[],
-                    help="V6 raster path(s) for comparison. Optional but recommended.")
-    ap.add_argument("--labels", required=True,
-                    help="balanced_karst_v8.parquet path")
-    ap.add_argument("--out", required=True, help="Output directory")
-    ap.add_argument("--holdout-fold", type=int, default=2,
-                    help="Which longitude band (0..4) to treat as held-out for spatial-CV AUC (default 2 = middle)")
-    args = ap.parse_args()
-
-    out = Path(args.out)
-    out.mkdir(parents=True, exist_ok=True)
-
-    print(f"[1] Loading labels from {args.labels}")
-    df = pd.read_parquet(args.labels)
-    print(f"    {len(df):,} rows  /  classes: {df['sample_category'].value_counts().to_dict() if 'sample_category' in df.columns else df['tag'].value_counts().to_dict()}")
-    # Column compat: parquet has 'tag' and 'sample_karst_score' from build script
-    if "sample_category" not in df.columns and "tag" in df.columns:
-        df["sample_category"] = df["tag"]
-
-    y = df["sample_karst_score"].values
-    lons = df["lon"].values.astype(float)
-    lats = df["lat"].values.astype(float)
-
-    print(f"\n[2] Resolving V8 rasters...")
-    v8_paths = expand_rasters(args.v8_rasters)
-    print(f"    found {len(v8_paths)} V8 raster(s):")
-    for p in v8_paths:
-        print(f"      {p}")
-
-    v6_paths = expand_rasters(args.v6_rasters) if args.v6_rasters else []
-    if v6_paths:
-        print(f"\n[3] Resolving V6 rasters...")
-        print(f"    found {len(v6_paths)} V6 raster(s):")
-        for p in v6_paths:
-            print(f"      {p}")
-
-    if not v8_paths:
-        print("ERROR: no V8 rasters found — pass --v8-rasters with valid paths/globs")
-        sys.exit(2)
-
-    print(f"\n[4] Sampling raster values at {len(df):,} label points...")
-    df["v8_score"] = sample_multi_raster(v8_paths, lons, lats)
-    print(f"    V8 valid samples: {(~np.isnan(df['v8_score'])).sum():,} / {len(df):,}")
-
-    if v6_paths:
-        df["v6_score"] = sample_multi_raster(v6_paths, lons, lats)
-        print(f"    V6 valid samples: {(~np.isnan(df['v6_score'])).sum():,} / {len(df):,}")
-    else:
-        df["v6_score"] = np.nan
-
-    print(f"\n[5] Value distribution fingerprints")
-    print(f"    (V6 ground truth from run2 was: mean=0.82 std=0.02 — flat regression-to-mean failure)")
-    fps = []
-    for name, col in [("V6", "v6_score"), ("V8", "v8_score")]:
-        fp = value_fingerprint(name, df[col].values)
-        fps.append(fp)
-        if fp.get("n", 0):
-            print(f"    {name}: n={fp['n']:,}  min={fp['min']:.4f}  mean={fp['mean']:.4f}  std={fp['std']:.4f}  max={fp['max']:.4f}")
-            print(f"         p1={fp['p1']:.4f}  p25={fp['p25']:.4f}  median={fp['median']:.4f}  p75={fp['p75']:.4f}  p99={fp['p99']:.4f}")
-    pd.DataFrame(fps).to_csv(out / "value_fingerprints.csv", index=False)
-
-    print(f"\n[6] AUCs — in-sample (all 40,989 labels)")
-    in_sample = []
-    for name, col in [("V6", "v6_score"), ("V8", "v8_score")]:
-        r = compute_aucs(y, df[col].values, f"{name}_in_sample")
-        in_sample.append(r)
-        if not np.isnan(r.get("roc_auc", np.nan)):
-            print(f"    {r['label']:>20s}  n={r['n']:>6,}  ROC-AUC={r['roc_auc']:.4f}  PR-AUC={r['pr_auc']:.4f}  pos-rate={r['positive_rate']:.3f}")
-        else:
-            print(f"    {r['label']:>20s}  no overlap / insufficient data")
-
-    print(f"\n[7] AUCs — spatial holdout (longitude fold {args.holdout_fold} of 5)")
-    df["fold"] = spatial_holdout(df, k=5)
-    holdout = df[df["fold"] == args.holdout_fold]
-    print(f"    holdout n={len(holdout):,}  lon range=[{holdout['lon'].min():.2f}, {holdout['lon'].max():.2f}]")
-    holdout_aucs = []
-    for name, col in [("V6", "v6_score"), ("V8", "v8_score")]:
-        r = compute_aucs(holdout["sample_karst_score"].values, holdout[col].values, f"{name}_holdout_fold{args.holdout_fold}")
-        holdout_aucs.append(r)
-        if not np.isnan(r.get("roc_auc", np.nan)):
-            print(f"    {r['label']:>30s}  n={r['n']:>6,}  ROC-AUC={r['roc_auc']:.4f}  PR-AUC={r['pr_auc']:.4f}")
-
-    pd.DataFrame(in_sample + holdout_aucs).to_csv(out / "auc_summary.csv", index=False)
-
-    print(f"\n[8] Plotting diagnostics...")
-    plot_score_histograms(df, "v8_score", "V8", out / "hist_v8_by_class.png")
-    print(f"    wrote hist_v8_by_class.png")
-    if v6_paths:
-        plot_score_histograms(df, "v6_score", "V6", out / "hist_v6_by_class.png")
-        print(f"    wrote hist_v6_by_class.png")
-
-    scores = {"V8": df["v8_score"].values}
-    if v6_paths:
-        scores["V6"] = df["v6_score"].values
-    plot_roc(y, scores, out / "roc_v8_v6.png")
-    print(f"    wrote roc_v8_v6.png")
-
-    if v6_paths:
-        plot_v8_v6_scatter(df, out / "scatter_v8_v6.png")
-        print(f"    wrote scatter_v8_v6.png")
-
-    # Save full per-point sample table
-    df_out = df[["lon", "lat", "task_name", "sample_category", "sample_karst_score", "v8_score", "v6_score", "fold"]]
-    df_out.to_csv(out / "per_point_scores.csv", index=False)
-    print(f"    wrote per_point_scores.csv  ({len(df_out):,} rows)")
-
-    print(f"\n{'=' * 70}")
-    print("VERDICT GUIDE:")
-    print(f"  V6 in-sample AUC was likely ~0.50–0.55 (the regression-to-mean failure)")
-    print(f"  V8 should target:")
-    print(f"    - in-sample AUC > 0.80  (model fits)")
-    print(f"    - holdout AUC   > 0.70  (model generalizes spatially)")
-    print(f"    - value std > 0.10 (real spatial discrimination, not flat 0.82±0.02)")
-    print(f"    - histogram visibly bimodal (modes near 0 and 1)")
-    print(f"{'=' * 70}")
-
-
-if __name__ == "__main__":
-    main()

stats.csv

@@ -1,12 +0,0 @@
-metric,value
-n_total,21707.0
-n_karst,10714.0
-n_other,10993.0
-class_max_min_ratio,9.53
-n_other,10993.0
-n_surface_depression,7213.0
-n_sinkhole,2348.0
-n_surface_mine,1153.0
-n_pa_karst_east,13483.0
-n_pa_karst_central,7502.0
-n_pa_karst_west,722.0

studio/import.geojson

@@ -1,3 +1,3 @@
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+size 14124005

studio/import.json

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+oid sha256:e2ce6b9ca7a2bc0e34a77f0d9aa4f96f74b6dee0d2f118e75c8186d32b466c17
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