Records: 15,731
Materialized samples: 15,731 .npy files
Included datasets: fire, burned_area, anomaly_detection, worldfloods, lc, roads
Missing dataset: building
Label cardinality: min 0, max 4, mean 1.2793

Per-dataset totals

fire: 1,600
burned_area: 1,299
anomaly_detection: 9,216
worldfloods: 2,353
lc: 63
roads: 1,200

Status counts

positive: 14,251
explicit_negative: 949
below_threshold: 531

Most frequent labels

water: 7,570
land: 4,789
cloud: 3,123
turbid_water: 1,623
road_present: 1,031
burned_area: 688

Analysis plots

The dataset graphs are stored in plots/:

coverage_distributions.png
dataset_label_heatmap.png
dataset_split_counts.png
label_cooccurrence.png
label_frequency.png
status_and_cardinality.png
plot_summary.json

Notes

Arrays are stored as .npy materializations.
The dataset was built from the current local phi2FM downstream sources and then uploaded to this Hugging Face dataset repository.
burned_area was rebuilt from the original OEOBench 256x256 source scenes. The raw routerset artifact now stores one native 7x256x256 burned-area record per selected source scene instead of eight derived 64x128 subpatches.
roads raw rows are rebuilt as 256x256x10 uint16 2x2 mosaics from the published 500_shot_roads archive. They keep native road_present coverage and add heuristic cloud / land / water weak labels under label_source = native+heuristic_weak.
anomaly_detection raw rows are rebuilt as deterministic 8x256x256 tiles from the original source zarr, with aligned edge tiles when needed, instead of 4096x4096 full-scene rows.
The local rebuild helpers are scripts/rebuild_routerset_burned_area_from_source.py and scripts/rebuild_routerset_roads_anomaly_from_source.py. They back up the existing metadata snapshot, rewrite the raw .npy files and manifests from source, refresh summary/audit metadata, and can publish the corrected snapshot to Hugging Face.
A corrected canonical 8x256x256 export for local training and audit can be generated with make routerset-materialize, which writes to outputs/routerset/materialized_256/ by default. Raw roads/lc records are mapped to the student Sentinel-2 layout and scaled by 1/10000; float-domain student experts (fire, burned_area, worldfloods, anomaly_detection) are channel-adapted and then min-max normalized per image before padding. The canonical raw routerset snapshot now stores native 256x256 burned-area scenes, 256x256 anomaly tiles, and 256x256 roads mosaics.
Add --selected-only to scripts/materialize_routerset_dataset.py when exporting only a subset of experts and you need a self-contained manifest without passthrough rows from the other tasks.
A clean variant can be generated with make routerset-materialize-clean, which writes manifest_256.jsonl, fault_rows_256.jsonl, and fault_report.json under outputs/routerset/materialized_256_clean/ by default.
A full tile-by-tile audit can be generated with make routerset-audit MATERIALIZED_DATASET_DIR=outputs/routerset/fix27March, which writes audit/tile_audit.jsonl, audit/audit_summary.json, and RGB / false-RGB plot previews under that dataset root.
A full file-by-file audit over the rebuilt raw routerset snapshot can be generated with make routerset-raw-audit ROUTERSET_DIR=routerset, which writes summary.json, file_audit.jsonl, sample_rows.json, and summary plots under routerset/audit_raw/.
An executed raw-sample gallery notebook can be generated with env PYTHONPATH=src .venv/bin/python scripts/generate_routerset_raw_audit_notebook.py --execute, which writes notebooks/routerset_raw_audit.ipynb.
The clean export only removes objective row-level artifact faults such as all-zero materialized tiles. Split-level problems, for example fire validation having no positive rows, stay reported as blockers instead of being rewritten silently.
The dedicated notebook for the rebuilt fix27March artifact is notebooks/routerset_fix27March_audit.ipynb.

Downloads last month: 10,049

Total file size:

44 GB