Add paper link, project page, and task categories

Hi! I'm Niels from the community science team at Hugging Face. This PR improves the dataset card for the UAV-IndoorCL dataset by adding:
- Metadata for the `object-detection` task category and relevant tags (`robotics`, `uav`, `continual-learning`).
- Links to the paper, project page, and GitHub repository.
- A brief description of the dataset content and its purpose for Class-Incremental Learning (CIL).
- BibTeX citation information for researchers.

README.md

@@ -1,3 +1,30 @@
----
-license: mit
----
+---
+license: mit
+task_categories:
+- object-detection
+tags:
+- robotics
+- uav
+- continual-learning
+---
+
+# UAV-IndoorCL
+
+This repository contains the dataset presented in the paper [Learning on the Fly: Replay-Based Continual Object Perception for Indoor Drones](https://huggingface.co/papers/2602.13440).
+
+[**Project Page**](https://spacetime-vision-robotics-laboratory.github.io/learning-on-the-fly-cl) | [**GitHub**](https://github.com/SpaceTime-Vision-Robotics-Laboratory/learning-on-the-fly-cl)
+
+## Dataset Summary
+UAV-IndoorCL is an indoor video dataset consisting of 14,400 frames capturing inter-drone and ground vehicle footage. It was specifically designed to support and benchmark Class-Incremental Learning (CIL) research for resource-constrained aerial platforms. The frames were annotated via a semi-automatic workflow with high labeling agreement and final manual verification, ensuring temporal coherence across sequences.
+
+## Citation
+If you use this dataset in your research, please cite the following paper:
+
+```bibtex
+@article{nae2026learningflyreplaybasedcontinual,
+    title   = {Learning on the Fly: Replay-Based Continual Object Perception for Indoor Drones},
+    author  = {Nae, Sebastian-Ion and Barbu, Mihai-Eugen and Mocanu, Sebastian and Leordeanu, Marius},
+    journal = {arXiv preprint arXiv:2602.13440},
+    year    = {2026}
+}
+```