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name: torchgeo description: "Use for TorchGeo geospatial deep learning work: datasets, samplers, data modules, transforms, pre-trained models, Lightning tasks, and contribution/testing workflows." metadata: disco-role: operating disable-model-invocation: true license: MIT

TorchGeo

Use this repo skill when working with the torchgeo Python package, a PyTorch domain library for geospatial and remote-sensing machine learning. The skill is grounded in the TorchGeo repository at commit 51a30d67f28794090f88a59b91deeadf91de1878 and is intended for later Researcher sessions; do not assume the original checkout is available.

Route by task

• Dataset composition, GeoDataset/NonGeoDataset, raster/vector IO, sample dictionaries, collate functions, patch samplers, ROI/TOI selection, or fake dataset fixtures: read datasets-and-samplers.
• Lightning DataModule usage, torchgeo CLI/JSONArgParse configs, task classes for classification, segmentation, detection, regression, self-supervised learning, or batch augmentation handoff: read datamodules-and-tasks.
• Pre-trained models, weight enums, get_model, get_weight, list_models, timm/SMP integration, spectral indices, SAR/color/spatial/temporal transforms, or optional model dependencies: read models-and-transforms.
• Adding or modifying TorchGeo code, dataset tests, docs tables, style/type checks, fake data generation, or project conventions: read contribution-and-testing.

High-signal operating facts

• TorchGeo has two dataset families:
  • GeoDataset instances are indexed by spatiotemporal slices and can be composed with & for intersection and | for union.
  • NonGeoDataset instances behave like torchvision-style benchmark datasets and are indexed by integer sample id.
• Dataset samples are dictionaries. Common keys are image, mask, label, bbox_xyxy, metadata keys such as crs/bounds, and task-specific keys. Do not assume a fixed schema without reading the dataset class or test fixture.
• Use stack_samples for geospatial sample dictionaries and task-specific collate helpers such as detection collation where the datamodule/test uses them.
• Patch samplers yield GeoSlice windows; RandomPatchSampler is for stochastic training chips and Grid/GriddedPatchSampler style samplers are for deterministic tiling/evaluation.
• The torchgeo command line entry point is backed by torchgeo.main:main and Lightning/JSONArgParse. Prefer a config file for reproducible task/datamodule/trainer runs.
• Most practical workflows require geospatial dependencies (rasterio, pyproj, shapely, geopandas) and ML dependencies (torch, torchvision, lightning, kornia, timm, segmentation-models-pytorch). Optional dataset/model extras are not always installed.
• Avoid downloads/network in routine tests unless the test is explicitly marked slow. The repository uses small generated fake fixtures under tests/data/<dataset>/.

Bundled references and scripts

• Repo provenance
• Troubleshooting
• Backend and environment plan
• Router metadata
• scripts/probe_torchgeo_install.py: inspect an installed TorchGeo environment without downloading data.
• scripts/minimal_geodataset_smoke.py: create a tiny in-memory GeoDataset/sampler smoke check when dependencies are installed.