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@@ -54,15 +54,19 @@ Most of these will need to return a tuple of `(lat, long, width, height, proj, c
 
 PyTorch data loaders have two parameters: `sampler` and `batch_sampler`. Most of the time, we only need to use a custom Sampler, and the default BatchSampler will work. We may need to use a custom BatchSampler when the batch axis is replaced by a time axis, for example.
 
-### RandomChipSampler
+### GeoSampler
+
+Base class for the following samplers. Uses tuple instead of int for passing to `__getitem__` of `GeoDataset`. Not intended for `VisionDataset`.
+
+### RandomGeoSampler
 
 Randomly sample chips from the region of interest. Useful for training.
 
-### GridChipSampler (SequentialSampler? CheckerboardChipSampler?)
+### GridGeoSampler (SequentialGeoSampler? CheckerboardGeoSampler?)
 
 Takes arguments like stride and chip size, and returns possibly overlapping chips. If stride > chip size, no overlap. Useful for prediction.
 
-### ExistingChipSampler
+### ExistingGeoSampler
 
 What if chips are already defined in the dataset? In that case, we will want to use those and index normally.