| | --- |
| | license: |
| | - cc-by-4.0 |
| | language: |
| | - en |
| | tags: |
| | - remote-sensing |
| | - planet |
| | - change-detection |
| | - spatiotemporal |
| | - deep-learning |
| | - video-compression |
| | pretty_name: DynamicEarthNet-video |
| | viewer: false |
| | --- |
| | |
| |
|
| | <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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| |  |
| | |
| | <b><p>This dataset follows the TACO specification.</p></b> |
| | </div> |
| |
|
| | <br> |
| |
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| |
|
| | # 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. |
| |
|
| | 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) | |
