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name: detection-segmentation-inference description: "Choose and run GeoAI inference APIs for segmentation, detection, prompt segmentation, RF-DETR, water/cloud/super-resolution, ONNX, and automatic HF-style image tasks." disable-model-invocation: true metadata: disco-role: operating license: MIT

Detection and Segmentation Inference

Use this sub-skill when the task is about running GeoAI inference on existing imagery, checkpoints, or model IDs. Do not use it for dataset creation, model training, or data preparation.

Start here

• Read Inference workflows to choose the API family.
• Read API reference for the exact entry points, defaults, and outputs.
• Read Troubleshooting when imports, model metadata, band order, overlap, device, or output format fail.
• Run inference_input_check.py for a read-only preflight before large rasters, offline models, or mixed-band inputs.
• If the user explicitly wants a validation plan before any model run, present the plan and wait for approval before loading weights, starting inference, or writing outputs.

Choose the route

• geoai.auto for HF-style image tasks, task auto-detection, and one-shot geospatial inference.
• geoai.train inference wrappers for local semantic segmentation, instance segmentation, and detector checkpoints.
• geoai.segment and geoai.sam.SamGeo for text prompts, boxes, clicks, and SAM-backed segmentation.
• geoai.object_detect for multi-class detector workflows and batch detection.
• geoai.rfdetr for RF-DETR detection and segmentation variants.
• geoai.water, geoai.tools.cloudmask, geoai.tools.multiclean, and geoai.tools.sr for water, cloud, cleanup, and super-resolution workflows.
• geoai.onnx for export and ONNXRuntime inference.

Keep out of scope

• Training dataset creation and model training.
• Data download, tiling, clipping, or raster/vector preparation workflows.
• Foundation-model feature extraction or VLM deep detail unless the VLM is the inference backend being run.
• NWPU training/reference scripts; use the safe preflight helper instead.

Common outputs

• GeoTIFF masks, probability maps, depth maps, and super-resolution rasters.
• GeoJSON, GPKG, Shapefile, FlatGeobuf, or Parquet vector outputs.
• Detection GeoDataFrames and class-score summaries.
• ONNX files with JSON sidecar metadata.