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Datasets:
tree-classification-irim
/
tree-classification-irim-dataset

Tasks:
Image Classification
Other
Modalities:
Image
Size:
100K<n<1M
Tags:
IGN
Aerial
Environement
License:
Dataset card Data Studio Files Community
1
rojberr commited on
Commit
6e3f7fb
·
1 Parent(s): bb0f737

data(lidar):Add lidar and fix Quercus_pubescens

Browse files
Files changed (20) hide show
  1. README.md +21 -0
  2. data/imagery-Quercus_pubescens.zip +2 -2
  3. data/lidar-Abies_alba.zip +3 -0
  4. data/lidar-Abies_nordmanniana.zip +3 -0
  5. data/lidar-Castanea_sativa.zip +3 -0
  6. data/lidar-Fagus_sylvatica.zip +3 -0
  7. data/lidar-Larix_decidua.zip +3 -0
  8. data/lidar-Picea_abies.zip +3 -0
  9. data/lidar-Pinus_halepensis.zip +3 -0
  10. data/lidar-Pinus_nigra.zip +3 -0
  11. data/lidar-Pinus_nigra_laricio.zip +3 -0
  12. data/lidar-Pinus_pinaster.zip +3 -0
  13. data/lidar-Pinus_sylvestris.zip +3 -0
  14. data/lidar-Pseudotsuga_menziesii.zip +3 -0
  15. data/lidar-Quercus_ilex.zip +3 -0
  16. data/lidar-Quercus_petraea.zip +3 -0
  17. data/lidar-Quercus_pubescens.zip +3 -0
  18. data/lidar-Quercus_robur.zip +3 -0
  19. data/lidar-Quercus_rubra.zip +3 -0
  20. data/lidar-Robinia_pseudoacacia.zip +3 -0
README.md CHANGED
@@ -1,3 +1,24 @@
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  ---
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  license: apache-2.0
 
 
 
 
 
 
 
 
 
 
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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  license: apache-2.0
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+ pretty_name: tree-classification-irim-dataset
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+ size_categories:
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+ - 100K<n<1M
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+ task_categories:
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+ - image-classification
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+ - other
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+ tags:
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+ - IGN
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+ - Aerial
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+ - Environement
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  ---
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+
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+ # tree-classification-irim-dataset
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+
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+ This dataset is associated with tree-classification-irim university project and [GitHub Repository](https://github.com/GHOST-Science-Club/tree-classification-irim)
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+
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+ ## Dataset content
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+ This dataset consists of a high-resolution aerial image (250 pixels x 250 pixels) at 0.2 m resolution, based on [PureForest](https://huggingface.co/datasets/IGNF/PureForest/tree/main)
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+
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+ ### Annotations
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+ This dataset is used as DVC storage and may change from commit to commit as we modify our data trying to get the most accurate model predictions.
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+
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