Update README.md

README.md

@@ -60,17 +60,19 @@ tags:
 - biology
 - ecology
 - forest
-pretty_name: OAM-TCD
+pretty_name: "OAM-TCD: A globally diverse dataset of high-resolution tree cover maps"
 size_categories:
 - 1K<n<10K
 ---
-# Dataset Card for OAM-TCD
+# Dataset Card for OAM-TCD: A globally diverse dataset of high-resolution tree cover maps
+
+![Example annotation for image 1445]( example_test_annotation_1445.jpg)
 
 ## Dataset Details
 
- OAM-TCD is a dataset of high-resolution (10 cm/px) tree cover maps with instance-level masks for 280k trees and 56k tree groups.
+OAM-TCD is a dataset of high-resolution (10 cm/px) tree cover maps with instance-level masks for 280k trees and 56k tree groups.
  
- Images in the dataset are provided as 2048x2048 px RGB GeoTIFF tiles. The dataset can be used to train both instance segmentation models and semantic segmentation models.
+Images in the dataset are provided as 2048x2048 px RGB GeoTIFF tiles. The dataset can be used to train both instance segmentation models and semantic segmentation models.
 
 ### Dataset Description
 
@@ -78,18 +80,25 @@ size_categories:
 - **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.
 
- 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.
 
 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.
 
-### Dataset Sources [optional]
+### Dataset Sources
 
 All imagery in the dataset is sourced from OpenAerialMap (OAM, part of the Open Imagery Network / OIN).
 
 ## Uses
 
-We anticipate that most users of the dataset wish to map tree cover in aerial orthomosaics, either captured by drones/unmanned aerial vehicles (UAVs) or from aerial surveys such as those provided by governmental organisations.
 
+![Prediction map over city of Zurich using a model trained on OAM-TCD](zurich_predictions_side_by_side_small.jpg)
+
+_Tree semantic segmentation for Zurich, predicted at 10 cm/px. Predictions with a confidence
+of < 0.4 are hidden. Left - 10 cm RGB orthomosaic provided by the Swiss Federal Office of
+Topography swisstopo/SWISSIMAGE 10 cm (2022), Right - prediction heatmap using `restor/tcd-segormer-mit-b5`.
+Base map tiles by Stamen Design, under CC BY 4.0. Data by OpenStreetMap, under ODbL._
+
+We anticipate that most users of the dataset wish to map tree cover in aerial orthomosaics, either captured by drones/unmanned aerial vehicles (UAVs) or from aerial surveys such as those provided by governmental organisations.
 
 ### Direct Use
 
@@ -152,6 +161,26 @@ The image tiles in the dataset contain geospatial information which is not obfus
 
 An argument for keeping accurate geospatial information is that annotations can be verified against independent sources, for example global land cover maps. The annotations can also be combined with other datasets like multispectral satellite imagery or products like Global Ecosystem Dynamics Investigation (GEDI, Dubayah et. al, 2020)
 
+## General dataset statistics
+
+The dataset contains 5072 image tiles sourced from OpenAerialMap; of these 4608 are licensed as CC-BY 4.0, 272 are licensed as CC BY-NC 4.0 and 192 are licensed as CC BY-SA 4.0. As described earlier, we split these images into separate repositories to keep licensing distinct. Only around 5% of imagery in the training split has a less permissive non-commercial license and we are re-training models on only the CC-BY portion of the data to maximise accessibility and re-use.
+
+The training dataset split contains 4406 images and the test split contains 666 images. All images are the same size (2048x2048 px) and the same ground sample distance (10 cm/px). The geographic distribution of the dataset is shown below:
+
+![Global distribution of annotations in the OAM-TCD dataset](annotation_map.png)
+_Global distribution of annotations in the OAM-TCD dataset_
+
+Table 1, below, shows the number of tiles that correspond to each of the 14 terrestrial biomes described by (Olson et. al, 2021).
+
+The majority of the dataset covers (1) tropical and temperate broadleaf forest. Some biomes are clearly under-represented - notably (6) boreal forest/taiga; (9) flooded grasslands and savannas; (11) tundra; and (14) mangrove. Some of these biomes, mangrove in particular, are likely under-represented due to our sampling method (by binned location), as their geographic extent is relatively small. These statistics could be used to guide subsequent data collection in a more targeted fashion.
+
+
+![Biome distribution](biome_distribution_table.jpeg)
+_Distribution of images in terrestrial biomes, and in each of the suggested cross-validation folds_
+
+It is important to note that the biome classification is purely spatial and without inspecting images individually, one cannot make assumptions about what type of landscape was actually imaged, or if it is a natural ecosystem representative of that biome. We do not currently annotate images with a land use category, but this would potentially be a useful secondary measure of diversity in the dataset.
+
+
 ## Bias, Risks, and Limitations
 
 There are several potential sources of bias in our dataset. The first is geographic, related to where users of OAM are likely to capture data - accessible locations that are amenable to UAV flights. Some locations and countries place strong restrictions on UAV possession and use, for example. One of the use-cases for OAM is providing traceable imagery for OpenStreetMap which is also likely to bias what sorts of scenes users capture.
@@ -187,3 +216,12 @@ Josh Veitch-Michaelis (josh [at] restor.eco)
 ## Dataset Card Contact
 
 Please contact josh [at] restor.eco if you have any queries about the dataset, including requests for image removal if you believe your rights have been infringed.
+
+
+### Further Examples
+
+![Example annotation for image 1445]( example_test_annotation_1594.jpg)
+![Example annotation for image 2242]( example_test_annotation_2242.jpg)
+![Example annotation for image 555]( example_test_annotation_555.jpg)
+_Annotation examples in OAM-TCD (IDs 1445, 2242, 555), all RGB images licensed CC BY-4.0, attribution contributors of OIN)_
+