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name: data-preparation description: "Guides RoboSat data preparation for Slippy Map imagery, OSM features, rasterized masks, tile CSVs, dataset configs, class weights, and safe CLI validation." disable-model-invocation: true metadata: disco-role: operating license: MIT

Data Preparation

Use this sub-skill when you need RoboSat dataset inputs, not model training or post-processing.

Use this for

• extracting OSM features into GeoJSON
• building tile cover CSVs
• downloading imagery into Slippy Map directories
• rasterizing features into masks
• computing class weights from training labels
• validating tile layout and tile-list alignment
• drafting dataset TOML templates

Route elsewhere

• model lifecycle: training, export, predict, serve, checkpoints, UNet
• feature post-processing: probability-to-mask, features, merge, dedupe, compare

Quick path

1. Start with the recipes in workflows.
2. Check the data layouts in data formats.
3. Use the API notes in API reference when wiring helpers.
4. Run validate_slippy_map.py before weights or rasterize jobs.
5. Use troubleshooting for install, geometry, sync, or zoom problems.

Common outputs

• features.geojson
• tiles.csv
• Slippy Map image and mask trees
• dataset TOML with [common] and [weights]
• class weights list from rs weights

Safe defaults

• Prefer rs or python -m robosat.tools ... from an installed environment.
• Keep downloads, tokens, and large datasets outside the skill tree.
• Treat rs rasterize as binary-only in this release.