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name: geospatial-data-pipelines description: "Operate GeoAI geospatial data inspection, downloads, raster/vector conversion, JSON/YAML batch pipelines, and geospatial I/O troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: MIT

Geospatial Data Pipelines

Use this sub-skill when the task is about preparing or validating geospatial data for GeoAI: inspecting raster/vector files, obtaining STAC/NAIP/Overture data, checking CRS/bounds/bands, clipping or tiling rasters, vectorizing masks, rasterizing vectors, and validating or running GeoAI batch pipelines from JSON/YAML.

Route here for

• geoai info and Python inspection helpers for raster/vector metadata, band statistics, CRS, bounds, feature counts, attributes, and supported formats.
• geoai.download workflows for Planetary Computer STAC searches/downloads, NAIP imagery, Overture Maps features, asset listing/reading, vector conversion, and bounded download validation.
• geoai.pipeline batch jobs: Pipeline, FunctionStep, GlobStep, SemanticSegmentationStep, RasterToVectorStep, JSON/YAML config loading, checkpoint semantics, and on_error policy.
• geoai.utils raster/vector/sampling/visualization helpers for local GeoTIFF/GeoJSON/GeoPackage work, raster-to-vector and vector-to-raster conversion, TorchGeo-style sampling setup, map/plot inspection, and output QA.
• Repairing hard I/O cases such as an unknown pipeline step or a CRS mismatch before tiling/vectorizing.

Route away from this sub-skill

• Model inference details, checkpoint/model selection, RF-DETR/SAM/semantic segmentation tuning: route to detection-segmentation-inference.
• Training, label schema design, dataset splits, augmentation, metrics, and model fine-tuning: route to training-and-finetuning.
• Foundation model embeddings, DINOv3/Prithvi/TESSERA/UniverSat, VLMs, and model registry loading: route to foundation-models-embeddings-vlms.
• QGIS plugin, MCP server, provider credentials, and agent integrations: route to integrations-agents-qgis-mcp.

Start sequence

1. Identify the data object and operation: inspect, download, clip, rasterize, vectorize, sample, visualize, or run a batch config.
2. Validate inputs before mutation: file exists, extension is supported, CRS is present, bbox order is correct, output directories are intentional, and network/model/training side effects are acceptable.
3. Use the self-contained references:
   • Data and pipeline workflows
   • API reference
   • Troubleshooting
4. Use bundled helpers when useful:
   • scripts/validate_pipeline_config.py validates JSON/YAML pipeline configs, shows registered step types, and checks checkpoint config hashes without running a pipeline.
   • scripts/geospatial_io_smoke.py creates tiny local fixtures or inspects provided files to smoke-test raster/vector I/O and CRS compatibility.
5. Run model inference only after the geospatial data and config are validated; then hand off to the inference sub-skill if the task becomes model-centric.

Safety defaults

• Do not download from the network, start training, load model weights, or overwrite user data unless the user explicitly requested that side effect.
• If the user asks for a validation plan before any download or model run, present the plan and stop before any network, download, or inference side effect until the user explicitly approves it.
• Treat CLI download as a thin convenience wrapper. Prefer the Python APIs in geoai.download when precise output-directory handling, STAC asset control, or robust error recovery matters.
• Treat geoai pipeline show and the bundled validator as safe config checks; geoai pipeline run may create outputs, checkpoint files, and run model steps.
• For every raster/vector conversion, compare CRS and bounds first. If CRS differs, reproject the vector or bbox into the raster CRS before clipping, rasterizing, or vectorizing.