---
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;">

![Dataset Image](assets/taco.png)
  
<b><p>This dataset follows the TACO specification.</p></b>
</div>

<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.

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)         |