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README.md

@@ -55,4 +55,36 @@ configs:
   data_files:
   - split: train
     path: data/train-*
+license: cc-by-4.0
+task_categories:
+- image-segmentation
+size_categories:
+- n<1K
 ---
+# Lautx Tree Segmentation
+
+LAUTx: personal laser scanning (PLS) point clouds of six forest plots in Austria, used as a test set for SegmentAnyTree, a sensor- and platform-agnostic deep learning model for individual tree segmentation in laser scanning data. Each plot (33-85 million points) is split into a 3x3 grid of 14 m tiles, one row per tile (local coordinates in metres), with intensity, a per-point semantic label (ground, unlabeled, tree) and two versions of per-point tree instance ids: the updated TreeLearn labels (mask_2) and the original manual labels (mask_3). The dataset contains 54 images with pixel-level mask annotations.
+
+This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library.
+
+## Citation
+
+```bibtex
+@article{wielgosz2024segmentanytree,
+  title={SegmentAnyTree: A sensor and platform agnostic deep learning model for tree segmentation using laser scanning data},
+  author={Wielgosz, Maciej and Puliti, Stefano and Xiang, Binbin and Schindler, Konrad and Astrup, Rasmus},
+  journal={Remote Sensing of Environment},
+  volume={313},
+  pages={114367},
+  year={2024},
+  publisher={Elsevier}
+}
+```
+
+
+The dataset itself can be cited as:
+
+Tockner Andreas, Burmeister, J.-M., Gollob Christoph, Ritter Tim, &amp; Nothdurft Arne. (2025). *LAUTx - Individual Tree Point Clouds From Austrian Forest Inventory Plots* (Version v2) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.16910335
+
+
+*This dataset was reformatted from its original format to match HuggingFace standards.*