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name: data-and-models description: "Guides Raster Vision agents through ClassConfig, SceneConfig, DatasetConfig, RasterioSourceConfig, GeoJSONVectorSourceConfig, RasterizedSourceConfig, label sources, label stores, AOIs, GeoDataset, Learner, Predictor, and ScenePredictor APIs for geospatial ML." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

data-and-models

Use this sub-skill when the task is about Raster Vision's programmatic geospatial ML data/model layer:

• assembling or validating ClassConfig, SceneConfig, or DatasetConfig
• wiring RasterioSourceConfig, GeoJSONVectorSourceConfig, RasterizedSourceConfig, label sources, label stores, AOIs, or transformers
• using GeoDataset, Learner, or model-bundle inference APIs directly from Python

Route away

• Pipeline CLI orchestration, rastervision run ..., or command scheduling -> pipeline-cli
• example recipes, backend pipeline configs, or task-specific training configs -> pytorch-workflows
• S3 / Batch / SageMaker / filesystem runner plumbing -> cloud-and-filesystems

Read first

• Core data API
• Data formats
• PyTorch learner API
• Troubleshooting

Runtime helper

Use scripts/check_scene_config.py to lint a SceneConfig JSON before deeper debugging.

What to do

1. Identify whether the user is building scenes/labels directly, preparing learner data, or calling predictor APIs.
2. Validate SceneConfig / ClassConfig structure before suggesting a full pipeline run.
3. Prefer direct library usage over pipeline CLI when the task stays at the data/model layer.
4. Keep the output self-contained inside this skill tree.