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--- |
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license: |
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- cc-by-4.0 |
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language: |
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- en |
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tags: |
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- remote-sensing |
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- planet |
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- change-detection |
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- spatiotemporal |
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- deep-learning |
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- video-compression |
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pretty_name: DynamicEarthNet-video |
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viewer: false |
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--- |
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<div style="text-align: center; border: 1px solid #ddd; border-radius: 10px; padding: 15px; max-width: 250px; margin: auto; background-color: #f9f9f9;"> |
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 |
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<b><p>This dataset follows the TACO specification.</p></b> |
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</div> |
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<br> |
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# DynamicEarthNet-video: Daily PlanetFusion Image Cubes Compressed as Videos |
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## Description |
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### 📦 Dataset |
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DynamicEarthNet-video is a storage-efficient re-packaging of the original **DynamicEarthNet** collection. |
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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. |
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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: |
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| Version | Size | PSNR | Ratio | |
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| --------------------------- | ---------: | ------: | ----: | |
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| Raw GeoTIFF | 525 GB | — | 1 × | |
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| **DynamicEarthNet-video** | **8.5 GB** | 60.1 dB | 62 × | |
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| Extra-compressed (optional) | 2.1 GB | 54 dB | 249 × | |
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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. |
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The compact video format therefore removes I/O bottlenecks and enables: |
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* end-to-end training of sequence models directly from disk, |
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* rapid experimentation on 4-band daily time-series, |
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* efficient sharing of benchmarks for change detection and forecasting. |
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### 🛰️ Sensors |
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| Instrument | Platform | Bands | Native GSD | Role | |
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| ---------------- | --------------------------- | --------- | ---------- | -------------------- | |
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| **PlanetFusion** | PlanetScope / SkySat fusion | RGB + NIR | 3 m | Daily image sequence | |
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## 👤 Creators |
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| Name | Affiliation | |
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| ---------------------- | ------------------------------------ | |
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| Achraf Toker | Technical University of Munich (TUM) | |
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| Lisa Kondmann | TUM | |
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| Manuel Weber | TUM | |
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| Martin Eisenberger | TUM | |
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| Alfonso Camero | TUM | |
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| Jing Hu | TUM | |
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| André Pregel Höderlein | TUM | |
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| Çagatay Şenaras | Planet Labs PBC | |
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| Tyler Davis | Planet Labs PBC | |
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| Daniel Cremers | TUM | |
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| Guido Marchisio | Planet Labs PBC | |
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| Xiao Xiang Zhu | German Aerospace Center (DLR) / TUM | |
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| Laura Leal-Taixé | TUM | |
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## 📂 Original dataset |
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**Download (TUM Mediatum)**: [https://mediatum.ub.tum.de/1650201](https://mediatum.ub.tum.de/1650201) |
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## 🌮 Taco dataset |
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## ⚡ Reproducible Example |
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<a target="_blank" href="https://colab.research.google.com/drive/1V3kfJmbWJRVncQwbdqLKgDp4-adMVy4N?usp=sharing"> |
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<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> |
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</a> |
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```python |
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import tacoreader |
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import xarrayvideo as xav |
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import xarray as xr |
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import matplotlib.pyplot as plt |
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# Load tacos |
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table = tacoreader.load("tacofoundation:dynamicearthnet-video") |
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# Read a sample row |
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idx = 0 |
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row = dataset.read(idx) |
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row_id = dataset.iloc[idx]["tortilla:id"] |
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``` |
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<center> |
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<img src="assets/example.png" width="100%" /> |
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</center> |
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## 🛰️ Sensor Information |
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Sensors: **planet** |
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## 🎯 Task |
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* **Semantic change detection** and **land-cover mapping** on daily 4-band sequences. |
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* Benchmarks include U-TAE, U-ConvLSTM, 3D-UNet (official splits A/B/C) . |
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* DynamicEarthNet-video can also serve for next-frame prediction and self-supervised representation learning on high-frequency optical data. |
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## 📚 References |
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### Publication 01 |
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* **DOI**: [10.48550/arXiv.2203.12560](https://doi.org/10.48550/arXiv.2203.12560) |
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* **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]) |
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* **BibTeX Citation** |
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```bibtex |
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@inproceedings{toker2022dynamicearthnet, |
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title = {DynamicEarthNet: Daily Multi-Spectral Satellite Dataset for Semantic Change Segmentation}, |
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author = {Toker, Aykut and Kondmann, Leonie and Weber, Markus and Eisenberger, Marvin and Camero, Alejandro and others}, |
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booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, |
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year = {2022}, |
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doi = {10.48550/arXiv.2203.12560} |
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} |
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``` |
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## 💬 Discussion |
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Chat with the maintainers: [https://huggingface.co/datasets/tacofoundation/DynamicEarthNet-video/discussions](https://huggingface.co/datasets/tacofoundation/DynamicEarthNet-video/discussions) |
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## 🤝 Data Providers |
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| Name | Role | URL | |
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| --------------- | ---------------- | ------------------------------------------------ | |
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| Planet Labs PBC | Imagery provider | [https://www.planet.com](https://www.planet.com) | |
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## 👥 Curators |
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| Name | Organization | URL | |
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| ------------------------ | ------------------------- | ---------------------------------------------------------------------------------------------- | |
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| Oscar J. Pellicer-Valero | Image Signal Processing (ISP) | [Google Scholar](https://scholar.google.com/citations?user=CCFJshwAAAAJ&hl=en) | |
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| Cesar Aybar | Image Signal Processing (ISP) | [Google Scholar](https://scholar.google.es/citations?user=rfF51ocAAAAJ&hl=es) | |
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| Julio Contreras | Image Signal Processing (ISP) | [GitHub](https://github.com/JulioContrerasH) | |
