Update README.md

README.md

@@ -78,12 +78,18 @@ Images in the dataset are provided as 2048x2048 px RGB GeoTIFF tiles. The datase
 
 - **Curated by:** Restor / ETH Zurich
 - **Funded by:** Restor / ETH Zurich , supported by a Google.org AI for Social Good grant (ID: TF2012-096892, AI and ML for advancing the monitoring of Forest Restoration)
-- **License:** nnotations are predominantly released under a CC-BY 4.0 license, with around 10% licensed as CC BY-NC 4.0 or CC BY-SA 4.0. These less permissive images are distributed in separate repositories to avoid any ambiguity for downstream use.
+- **License:** CC-BY 4.0
 
-OIN declares that all imagery contained within is licensed as [CC-BY 4.0](https://github.com/openimagerynetwork/oin-register) however some images are labelled as CC BY-NC 4.0 or CC BY-SA 4.0 in their metadata.
+
+OIN declares that all imagery contained within is licensed as [CC-BY 4.0](https://github.com/openimagerynetwork/oin-register) however some images are labelled as CC BY-NC 4.0 or CC BY-SA 4.0 in their metadata. Annotations are predominantly released under a CC-BY 4.0 license, with around 10% licensed as CC BY-NC 4.0 or CC BY-SA 4.0. These less permissive images are distributed in separate repositories to avoid any ambiguity for downstream use.
 
 To ensure that image providers' rights are upheld, we split these images into license-specific repositories, allowing users to pick which combinations of compatible licenses are appropriate for their application. We have initially released model variants that are trained on CC BY + CC BY-NC imagery. CC BY-SA imagery was removed from the training split, but it can be used for evaluation.
 
+The other repositories/datasets are:
+
+- `restor/tcd-nc` containing only `CC BY-NC 4.0` licensed images
+- `restor/tcd-sa` containing only `CC BY-SA 4.0` licensed images
+  
 ### Dataset Sources
 
 All imagery in the dataset is sourced from OpenAerialMap (OAM, part of the Open Imagery Network / OIN).
@@ -112,7 +118,7 @@ The dataset does not directly support applications related to carbon sequestrati
 
 ## Dataset Structure
 
-The dataset contains pairs of images, semantic masks and object segments (instance polygons). The masks contain instance-level annotations for (1) individual **tree**s and (2) groups of trees, which we label **canopy**. For training our models we binarise the masks. Metadata from OAM for each image is provided and described below.
+The dataset contains pairs of images, semantic masks and object segments (instance polygons). The masks contain instance-level annotations for (1) individual **trees** and (2) groups of trees, which we label **canopy**. For training our models we binarise the masks. Metadata from OAM for each image is provided and described below.
 
 The dataset is released with suggested training and test splits, stratified by biome. These splits were used to derive results presented in the main paper. Where known, each image is also tagged with its terrestrial biome index [-1, 14]. This relationship was defined by looking for intersections between tile polygons and reference biome polygons, an index of -1 means a biome wasn't able to be matched. Tiles sourced from a given OAM image are isolated to a single fold (and split) to avoid train/test leakage.