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name: feature-postprocessing description: "Guides RoboSat prediction post-processing from probability PNGs to masks, parking GeoJSON features, merge/dedupe cleanup, visual comparison, and vector artifact validation." disable-model-invocation: true metadata: disco-role: operating license: MIT

RoboSat feature post-processing

Use this sub-skill when a task already has RoboSat prediction probability tiles or mask tiles and needs to turn them into inspectable masks, parking-lot vector features, cleaned GeoJSON, or visual QA mosaics.

Use this for

• Converting binary foreground-probability PNG Slippy Map outputs into palette mask tiles with rs masks.
• Extracting parking-lot WGS84 GeoJSON polygons from mask tiles with rs features --type parking.
• Merging adjacent predicted polygons with a meter threshold via rs merge.
• Deduplicating predictions against existing OSM-derived GeoJSON with an IoU threshold via rs dedupe.
• Building side-by-side image / label / mask mosaics with rs compare.
• Validating post-processing artifacts using bundled scripts.

Route elsewhere

• Need to train a model, load checkpoints, export ONNX, run batch prediction, or produce probability PNGs first: use the model-lifecycle sub-skill.
• Need to extract OSM features before training, cover tiles, download imagery, rasterize labels, compute class weights, or subset tile sets: use the data-preparation sub-skill.
• Need non-parking post-processing handlers: this sub-skill explains the handler contract, but the installed CLI only registers the parking handler by default.

Fast workflow map

1. Confirm that probability or mask directories are Slippy Map trees such as 18/69108/105091.png; see data formats.
2. Convert probabilities to masks: rs masks out/masks probs/run-a [probs/run-b ...] [--weights 0.7 0.3].
3. Convert parking masks to GeoJSON: rs features out/masks --type parking --dataset dataset.toml out/features.geojson.
4. Clean vector output: rs merge out/features.geojson --threshold 3 out/features-merged.geojson, then optionally rs dedupe osm.geojson out/features-merged.geojson --threshold 0.5 out/features-new.geojson.
5. Compare visual quality: rs compare out/compare images labels out/masks [other/masks ...] --minimum 0.01 --maximum 0.95.
6. Validate artifacts with scripts/validate_feature_collection.py and smoke-test soft voting with scripts/softvote_smoke.py.

Detailed command recipes are in workflows. API behavior is summarized in API reference. Common failure modes are in troubleshooting.

Key constraints to remember

• RoboSat probability PNG post-processing is binary: one quantized foreground-probability channel is reconstructed as foreground plus 1 - foreground background.
• rs features is parking-only in the default installed CLI; the dataset config must include the parking class and mask class index must match it.
• ParkingHandler thresholds are tuned for zoom 18 and will raise on other zoom levels.
• rs merge --threshold is measured in meters; rs dedupe --threshold is intersection-over-union, not distance or model confidence.
• Post-processing is CPU-oriented. CUDA is not needed for the commands in this sub-skill.