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README: root -> paths for GeoDatasets #2438

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6 changes: 3 additions & 3 deletions README.md
Original file line number Diff line number Diff line change
Expand Up @@ -72,15 +72,15 @@ Many remote sensing applications involve working with [_geospatial datasets_](ht
In this example, we show how easy it is to work with geospatial data and to sample small image patches from a combination of [Landsat](https://www.usgs.gov/landsat-missions) and [Cropland Data Layer (CDL)](https://data.nal.usda.gov/dataset/cropscape-cropland-data-layer) data using TorchGeo. First, we assume that the user has Landsat 7 and 8 imagery downloaded. Since Landsat 8 has more spectral bands than Landsat 7, we'll only use the bands that both satellites have in common. We'll create a single dataset including all images from both Landsat 7 and 8 data by taking the union between these two datasets.

```python
landsat7 = Landsat7(root="...", bands=["B1", ..., "B7"])
landsat8 = Landsat8(root="...", bands=["B2", ..., "B8"])
landsat7 = Landsat7(paths="...", bands=["B1", ..., "B7"])
landsat8 = Landsat8(paths="...", bands=["B2", ..., "B8"])
landsat = landsat7 | landsat8
```

Next, we take the intersection between this dataset and the CDL dataset. We want to take the intersection instead of the union to ensure that we only sample from regions that have both Landsat and CDL data. Note that we can automatically download and checksum CDL data. Also note that each of these datasets may contain files in different coordinate reference systems (CRS) or resolutions, but TorchGeo automatically ensures that a matching CRS and resolution is used.

```python
cdl = CDL(root="...", download=True, checksum=True)
cdl = CDL(paths="...", download=True, checksum=True)
dataset = landsat & cdl
```

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