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name: robosat description: "Guides RoboSat aerial and satellite imagery segmentation workflows with Slippy Map data preparation, U-Net model lifecycle, feature post-processing, CLI usage, configs, and legacy environment troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: MIT

RoboSat

Use this repo skill when a task involves RoboSat, the robosat Python package, the rs CLI, aerial/satellite imagery semantic segmentation, Slippy Map tile trees, OSM-derived feature masks, U-Net checkpoints, probability PNGs, or GeoJSON feature extraction.

RoboSat is a legacy Mapbox package for feature extraction from aerial and satellite imagery. It organizes data as Slippy Map tiles, trains a PyTorch U-Net-style segmentation model, predicts binary foreground probabilities, and post-processes masks into GeoJSON features.

Start here

1. Read installation and environment when installing, importing, checking the CLI, choosing CPU/CUDA, or debugging old dependency stacks.
2. Read CLI reference to route an rs command or build a command line.
3. Read configuration before editing dataset/model TOML files.
4. Use troubleshooting for cross-cutting install, CRS, tile layout, backend, token, or binary-class issues.
5. Use repo provenance before refreshing this skill against a newer checkout.

Route by task

User task	Read
Extract OSM buildings/roads/parking, create tile CSVs, download imagery, rasterize labels, validate tile trees, compute class weights, or subset image/label tiles	data-preparation
Train or resume U-Net models, export ONNX, run batch prediction, serve mask tiles, inspect losses/metrics/transforms/checkpoints, or decide CPU vs CUDA	model-lifecycle
Convert probability PNGs to masks, extract parking GeoJSON, merge/dedupe predicted polygons, compare image/label/mask mosaics, or validate vector outputs	feature-postprocessing

Minimal environment check

After installing RoboSat in an environment, prefer the installed package entry points rather than a source-checkout wrapper:

python -c "import robosat; print('robosat import ok')"
rs --help
python -m robosat.tools --help
python scripts/check_robosat_env.py --check-cli

The bundled check_robosat_env.py checks imports, rtree/libspatialindex, pyproj ESRI CRS lookup, optional torch/CUDA state, and CLI help. Use create_config_templates.py to write CPU-safe dataset/model TOML templates.

Important constraints

• RoboSat is legacy Python-era software. Python 3.6 plus the documented runtime dependency family is the safest source-install target; Docker CPU/GPU images were the original operational path.
• GPU is optional in this skill. This skill generation verified CPU import, CLI help, and a CPU U-Net smoke only; it does not claim CUDA training/prediction verification.
• The default prediction and post-processing path is binary foreground/background. Several helpers assume two classes and parking-specific post-processing.
• Runtime instructions in this skill are self-contained; do not require the original repository checkout when using the installed package.

Common pipeline

1. Data preparation: OSM feature extraction -> tile cover CSV -> imagery download -> rasterized masks -> weights.
2. Model lifecycle: training or checkpoint resume -> optional ONNX export -> prediction probability tiles or on-demand serving.
3. Feature post-processing: probability tiles -> mask tiles -> parking GeoJSON -> merge/dedupe -> visual comparison.

Use the sub-skill routes above for concrete commands, APIs, data formats, validation scripts, and troubleshooting.