Update README.md

README.md

@@ -1,13 +1,165 @@
 ---
-license: mit
 arxiv: 2506.19656
+license:
+  - cc-by-4.0
+language:
+  - en
+tags:
+  - remote-sensing
+  - planet
+  - change-detection
+  - spatiotemporal
+  - deep-learning
+  - video-compression
+pretty_name: DynamicEarthNet-video
+viewer: false
 ---
 
-# WIP
 
-A tutorial on how to use this data will be coming out soon.
-In the meanwhile, please refer to the TACO Foundation page: https://huggingface.co/tacofoundation
+<div style="text-align: center; border: 1px solid #ddd; border-radius: 10px; padding: 15px; max-width: 250px; margin: auto; background-color: #f9f9f9;">
 
-## Citation
+![Dataset Image](assets/taco.png)
+  
+<b><p>This dataset follows the TACO specification.</p></b>
+</div>
 
-This dataset is related to the paper: [arXiv:2506.19656](https://arxiv.org/abs/2506.19656)
+<br>
+
+
+# DynamicEarthNet-video: Daily PlanetFusion Image Cubes Compressed as Videos
+
+## Description
+
+
+### 📦 Dataset
+
+DynamicEarthNet-video is a storage-efficient re-packaging of the original **DynamicEarthNet** collection.
+The archive covers seventy-five 1024 × 1024 px regions (≈ 3 m GSD) across the globe, sampled daily from **1 January 2018 to 31 December 2019**. Each day is delivered as four-band PlanetFusion surface-reflectance images (B04 Red, B03 Green, B02 Blue, B8A Narrow-NIR). Monthly pixel-wise labels annotate seven land-cover classes: impervious, agriculture, forest, wetlands, bare soil, water and snow/ice.
+
+This dataset is related to the paper: [arXiv:2506.19656](https://arxiv.org/abs/2506.19656)
+
+All original GeoTIFF stacks (≈ 525 GB) are transcoded with **[xarrayvideo](https://github.com/IPL-UV/xarrayvideo)** to 12-bit H.265/HEVC, yielding dramatic size savings while preserving scientific fidelity:
+
+| Version                     |       Size |    PSNR | Ratio |
+| --------------------------- | ---------: | ------: | ----: |
+| Raw GeoTIFF                 |     525 GB |       — |   1 × |
+| **DynamicEarthNet-video**   | **8.5 GB** | 60.1 dB |  62 × |
+| Extra-compressed (optional) |     2.1 GB |   54 dB | 249 × |
+
+Extensive tests show that semantic change-segmentation scores obtained with U-TAE, U-ConvLSTM and 3D-UNet remain statistically unchanged (Δ mIoU ≤ 0.02 pp) when the compressed cubes replace the raw imagery.
+
+The compact video format therefore removes I/O bottlenecks and enables:
+
+* end-to-end training of sequence models directly from disk,
+* rapid experimentation on 4-band daily time-series,
+* efficient sharing of benchmarks for change detection and forecasting.
+### 🛰️ Sensors
+
+| Instrument       | Platform                    | Bands     | Native GSD | Role                 |
+| ---------------- | --------------------------- | --------- | ---------- | -------------------- |
+| **PlanetFusion** | PlanetScope / SkySat fusion | RGB + NIR | 3 m        | Daily image sequence |
+
+
+## 👤 Creators
+
+
+| Name                   | Affiliation                          |
+| ---------------------- | ------------------------------------ |
+| Achraf Toker           | Technical University of Munich (TUM) |
+| Lisa Kondmann          | TUM                                  |
+| Manuel Weber           | TUM                                  |
+| Martin Eisenberger     | TUM                                  |
+| Alfonso Camero         | TUM                                  |
+| Jing Hu                | TUM                                  |
+| André Pregel Höderlein | TUM                                  |
+| Çagatay Şenaras        | Planet Labs PBC                      |
+| Tyler Davis            | Planet Labs PBC                      |
+| Daniel Cremers         | TUM                                  |
+| Guido Marchisio        | Planet Labs PBC                      |
+| Xiao Xiang Zhu         | German Aerospace Center (DLR) / TUM  |
+| Laura Leal-Taixé       | TUM                                  |
+
+
+## 📂 Original dataset
+
+**Download (TUM Mediatum)**: [https://mediatum.ub.tum.de/1650201](https://mediatum.ub.tum.de/1650201) 
+
+
+
+## 🌮 Taco dataset
+
+## ⚡ Reproducible Example
+
+<a target="_blank" href="https://colab.research.google.com/drive/1V3kfJmbWJRVncQwbdqLKgDp4-adMVy4N?usp=sharing">
+  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
+</a>
+
+```python
+import tacoreader
+import xarrayvideo as xav
+import xarray as xr
+import matplotlib.pyplot as plt
+
+# Load tacos
+table = tacoreader.load("tacofoundation:dynamicearthnet-video")
+
+# Read a sample row
+idx = 0
+row = dataset.read(idx)
+row_id = dataset.iloc[idx]["tortilla:id"]
+```
+
+<center>
+  <img src="assets/example.png" width="100%" />
+</center>
+
+
+## 🛰️ Sensor Information
+
+Sensors: **planet**
+
+## 🎯 Task
+
+
+* **Semantic change detection** and **land-cover mapping** on daily 4-band sequences.
+* Benchmarks include U-TAE, U-ConvLSTM, 3D-UNet (official splits A/B/C) .
+* DynamicEarthNet-video can also serve for next-frame prediction and self-supervised representation learning on high-frequency optical data.
+
+## 📚 References
+
+
+### Publication 01
+
+* **DOI**: [10.48550/arXiv.2203.12560](https://doi.org/10.48550/arXiv.2203.12560)
+* **Summary**: Toker *et al.* introduce **DynamicEarthNet**, a benchmark of 75 daily 4-band PlanetFusion image cubes (3 m, 2018-2019) with monthly 7-class land-cover masks for semantic‐change segmentation. The paper establishes U-TAE, U-ConvLSTM and 3D-UNet baselines and proposes spatially blocked cross-validation to limit autocorrelation. ([arXiv][1])
+* **BibTeX Citation**
+
+```bibtex
+@inproceedings{toker2022dynamicearthnet,
+  title     = {DynamicEarthNet: Daily Multi-Spectral Satellite Dataset for Semantic Change Segmentation},
+  author    = {Toker, Aykut and Kondmann, Leonie and Weber, Markus and Eisenberger, Marvin and Camero, Alejandro and others},
+  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
+  year      = {2022},
+  doi       = {10.48550/arXiv.2203.12560}
+}
+```
+
+
+## 💬 Discussion
+
+Chat with the maintainers: [https://huggingface.co/datasets/tacofoundation/DynamicEarthNet-video/discussions](https://huggingface.co/datasets/tacofoundation/DynamicEarthNet-video/discussions)
+
+
+## 🤝 Data Providers
+
+| Name            | Role             | URL                                              |
+| --------------- | ---------------- | ------------------------------------------------ |
+| Planet Labs PBC | Imagery provider | [https://www.planet.com](https://www.planet.com) |
+
+## 👥 Curators
+
+| Name                     | Organization              | URL                                                                                            |
+| ------------------------ | ------------------------- | ---------------------------------------------------------------------------------------------- |
+| Oscar J. Pellicer-Valero | Image Signal Processing (ISP) | [Google Scholar](https://scholar.google.com/citations?user=CCFJshwAAAAJ&hl=en) |
+| Cesar Aybar              | Image Signal Processing (ISP) | [Google Scholar](https://scholar.google.es/citations?user=rfF51ocAAAAJ&hl=es)                                         |
+| Julio Contreras       | Image Signal Processing (ISP) | [GitHub](https://github.com/JulioContrerasH)         |