Improve dataset card: Add task category, MAESTRO paper link and code link (#2)

- Improve dataset card: Add task category, MAESTRO paper link and code link (7480ff9e573cf022f05bccce395aff60e57e6ed9)


Co-authored-by: Niels Rogge <[email protected]>

README.md

@@ -1,10 +1,22 @@
 ---
 license: cc-by-4.0
+task_categories:
+- image-classification
+tags:
+- earth-observation
+- remote-sensing
+- satellite-imagery
+- time-series
+- multimodal
+- multitemporal
+- multispectral
 ---
 
 # TreeSatAI-Time-Series
 ****
-This dataset was introduced in the [ECCV24 paper](https://arxiv.org/pdf/2404.08351) Omnisat.
+This dataset was introduced in the [ECCV24 paper](https://arxiv.org/pdf/2404.08351) OmniSat.
+
+The dataset is utilized in the paper [MAESTRO: Masked AutoEncoders for Multimodal, Multitemporal, and Multispectral Earth Observation Data](https://huggingface.co/papers/2508.10894). The code for MAESTRO can be found at: https://github.com/ignf/maestro
 
 Ahlswede et al. (https://essd.copernicus.org/articles/15/681/2023/) introduced the TreeSatAI Benchmark Archive, a new dataset for tree species classification in Central Europe based on multi-sensor data from aerial, 
 Sentinel-1 and Sentinel-2. The dataset contains labels of 20 European tree species (*i.e.*, 15 tree genera) derived from forest administration data of the federal state of Lower Saxony, Germany. 
@@ -79,4 +91,16 @@ If you have any questions, issues or feedback, you can contact us at: ai-challen
   journal={ECCV},
   year={2024}
 }
+```
+
+### Paper Citation
+If you use this dataset in your work, please cite the MAESTRO paper:
+
+```bibtex
+@article{labatie2025maestro,
+  title={MAESTRO: Masked AutoEncoders for Multimodal, Multitemporal, and Multispectral Earth Observation Data},
+  author={Labatie, Antoine and Vaccaro, Michael and Lardiere, Nina and Garioud, Anatol and Gonthier, Nicolas},
+  journal={arXiv preprint arXiv:2508.10894},
+  year={2025}
+}
 ```