Feature/rasterized vectordataset support #2505

[sherif-med]

Summary

This PR introduces the RasterizedVectorDataset, a subclass of VectorDataset, to enable loading vector data as rasterized masks, addressing issue #2505 . It includes:

• ✅ Implementation of RasterizedVectorDataset
• ✅ Introduction of RasterizationStrategy and DefaultRasterizationStrategy
• ✅ Support for clipping geometries to the query window
• ✅ Updated documentation and tutorials
• ✅ Unit tests for rasterized datasets and clipping behavior

---

Motivation

Many geospatial tasks (e.g., semantic segmentation) require vector labels to be rasterized. This new dataset class simplifies workflows by seamlessly integrating rasterized vector data into TorchGeo’s dataset ecosystem.

---

Docs Updated

• docs/tutorials/custom_raster_dataset.ipynb: Added explanations for RasterizedVectorDataset
• docs/user/alternatives.rst: Added RasterizedVectorDataset to comparison overview
• API docstrings for all new classes

---

Tests

• Added tests in tests/datasets/test_geo.py for:
  • __getitem__
  • Querying with and without geometry clipping
  • Edge cases (e.g., empty window, invalid query)
  • Multilabel support
  • Proper rasterization output

---

Affected Files (Highlights)

• torchgeo/datasets/geo.py: Implementation of RasterizedVectorDataset, RasterizationStrategy
• torchgeo/datasets/cbf.py, eurocrops.py: Switched to use RasterizedVectorDataset
• torchgeo/datasets/__init__.py: Added new classes to exports
• Unit tests and docs as noted above

---

Example Usage

from torchgeo.datasets import RasterizedVectorDataset

dataset = RasterizedVectorDataset(
    root="data/vector",
    res=0.1,
    transforms=my_transforms,
    label_name="class_id",
    clip_geometries=True,
)
sample = dataset[dataset.bounds]

---

Future Enhancements

• Add a new rasterizer that converts polygons into mask-of-polygons with contours
• Integrate visualization tools for debugging geometry mask alignment

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[adamjstewart]

Why do we need RasterizedVectorDataset to solve #2505? VectorDataset already supports this feature.

[sherif-med]

As mentioned in #2505 some tasks require geometries instead of masks as input. Also the name VectorDataset is counter-intuitive as its elements are images/masks. VectorDataset should (in my opinion) return spatial features (including geometry and attributes)

[adamjstewart]

The names are based on supported file types, not returned keys.

The idea of #2505 is to modify VectorDataset such that a single class can be used for semantic/instance segmentation and object/keypoint detection. Otherwise, we would need 4 copies of EuroCrops (for example).

[sherif-med]

Great, do you think a switcher method within VectorDataset to alter the output of getitem dynamically is a good solution? In the case of object/keypoint detection, the returned items are not images/masks anymore. Should I take that into account? as different methods like "plot" expect a 'sample dict containing masks tensor'

[adamjstewart]

I think we should return all of the above. Then the plot method can also plot all potential outputs. The return value is a dictionary, so we can add as many keys as we want.

[adamjstewart]

Is this superseded by #2819?