---

name: raster-vision description: "Routes Raster Vision geospatial computer vision tasks involving rastervision CLI pipelines, GeoTIFF/GeoJSON data configs, PyTorch chip classification, semantic segmentation, object detection, model bundles, AWS runners, Docker, and S3/GDAL filesystems." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Raster Vision

Use this repo skill when a task involves Raster Vision as a library or low-code framework for deep learning on satellite, aerial, or other large geospatial imagery.

When to use

Read this skill for requests about:

• rastervision run, run_command, predict, or predict_scene
• ClassConfig, SceneConfig, DatasetConfig, RasterioSourceConfig, GeoJSONVectorSourceConfig, label sources/stores, AOIs, analyzers, evaluators, or model bundles
• PyTorch-backed chip classification, semantic segmentation, object detection, GeoDataset, Learner, backbones, solvers, and task examples
• Docker, AWS Batch, AWS SageMaker, S3, requester-pays/unsigned S3, optional GDAL VSI, CloudFormation, or remote Raster Vision job setup
• troubleshooting geospatial data, config validation, backend selection, and model-bundle prediction failures

Do not use this skill for generic computer vision frameworks unless Raster Vision's geospatial pipeline/config/file-system layer is part of the task.

Install and smoke check

Typical release install:

pip install rastervision

For realistic GPU training or geospatial dependency parity, prefer a Raster Vision PyTorch Docker image. After installation, run the bundled checker:

python scripts/check_rastervision_install.py

Read installation and package map for package splits, optional plugins, GPU notes, and configuration locations. Read repository provenance before deciding whether this skill is stale for a checkout.

Route map

Task	Read
CLI syntax, config modules, runners, --arg, --splits, run_command, predict, predict_scene, or model-bundle CLI prediction	pipeline-cli
Programmatic geospatial APIs: scenes, class configs, raster/vector sources, label sources/stores, AOIs, GeoDataset, Learner, Predictor, ScenePredictor	data-and-models
PyTorch chip classification, semantic segmentation, object detection, example recipes, task backends, solvers, backbones, model zoo, transfer learning	pytorch-workflows
Docker, AWS Batch, AWS SageMaker, S3, requester-pays/unsigned S3, GDAL VSI, CloudFormation templates, bootstrap setup	cloud-and-filesystems

Core concepts

Raster Vision can be used in two modes:

1. Library mode: assemble geospatial data sources, labels, PyTorch datasets, learners, predictors, and label stores directly in Python.
2. Framework mode: write a Python config module with get_config(runner, **kwargs) or get_configs(...), then run the pipeline with the rastervision CLI.

Standard pipeline stages are analyze, chip, train, predict, eval, and bundle. The final bundle contains model weights plus config needed for later prediction on new imagery.

Decision checklist

Before giving operational guidance, identify:

• Task family: chip classification, semantic segmentation, object detection, direct library API use, or cloud execution.
• Data form: raw geospatial scenes, AOI-delimited scenes, prechipped image data, model bundle, or remote URI set.
• Execution mode: inprocess for debugging, local for host Makefile execution, batch for AWS Batch, or sagemaker for SageMaker.
• Backend needs: CPU inspection/smoke, CUDA throughput, Docker parity, AWS credentials, S3 access, or optional GDAL VSI.
• Output expectations: train/, predict/, eval/, bundle/model-bundle.zip, GeoTIFF labels, vector outputs, or JSON metrics.

Bundled helpers

• scripts/check_rastervision_install.py: verify required package imports, CLI help, optional plugins, and torch CUDA visibility without training or submitting cloud jobs.
• pipeline config summarizer: inspect a config module or JSON without executing pipeline commands.
• scene config checker: validate or minimally build a serialized SceneConfig.
• example command printer: render safe example commands without running them.
• Docker command renderer: render a docker run command without executing Docker.

Troubleshooting entry points

Start with cross-cutting troubleshooting, then drill into the nearest sub-skill:

• CLI/config failures: pipeline-cli/references/troubleshooting.md
• data/label/model API failures: data-and-models/references/troubleshooting.md
• PyTorch/example/backend failures: pytorch-workflows/references/troubleshooting.md
• Docker/AWS/S3/GDAL failures: cloud-and-filesystems/references/troubleshooting.md

Safety boundaries

• Do not run full training, download external datasets, submit AWS jobs, or create CloudFormation stacks unless the user explicitly approves cost, time, credentials, and write targets.
• Prefer helpers that render or validate commands before executing them.
• Keep remote-run data in S3 or another remote filesystem; AWS Batch and SageMaker jobs cannot read arbitrary local paths.
• When GPU behavior matters, verify PyTorch CUDA visibility instead of assuming GPU support from hardware alone.