README.md

---
arxiv: 2506.19656
license: 
    - cc-by-4.0
language: 
    - en
tags:
    - remote-sensing
    - sentinel-2
    - climate-extremes
    - video-compression
    - deep-learning
---

<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>


# DeepExtremeCubes-video: Sentinel-2 Minicubes in Video Format for Compound-Extreme Analysis

## 📝 Description

### 📦 Dataset

**DeepExtremeCubes-video** is a storage-efficient, analysis-ready re-packaging of the original [DeepExtremeCubes](https://doi.org/10.5281/zenodo.1234567) collection.
All 42 k Sentinel-2 minicubes (2.56 km × 2.56 km, 2016-2022, 7 bands, 5-daily cadence) have been transcoded with **[xarrayvideo](https://github.com/IPL-UV/xarrayvideo)** into H.265/HEVC videos, achieving \~12 × lossless-perceptual compression (≈ 270 GB vs 2.3 TB) at ≈ 56 dB PSNR.

This dataset is related to the paper: [arXiv:2506.19656](https://arxiv.org/abs/2506.19656)

This compact representation removes the prime bottleneck for training deep-learning models on spatio-temporal Earth-observation data, while preserving scientific fidelity for tasks such as:

* **Impact mapping** of compound heat-wave & drought (CHD) events
* **Forecasting** vegetation stress during extremes with ConvLSTM / U-TAE models
* **Self-supervised pre-training** on long reflectance sequences

### 🛰️ Sensors

* **Sentinel-2 MSI (Level-2A surface reflectance)** – Bands B02, B03, B04, B05, B06, B07, B8A at 10 m & 20 m (upsampled)
* **ERA5-Land single-pixel time-series** (temperature, soil moisture, etc.)
* **Copernicus DEM 30 m** (static)
* **Cloud/SCL masks** from EarthNet Cloud-Mask v1

> **Note:** All dynamic variables (bands, masks, ERA5-Land) are encoded as multi-channel videos; static rasters (DEM, land-cover) remain as compressed GeoTIFFs.

## 👤 Creators

* Leipzig University – Remote Sensing Centre
* Image and Signal Processing group (UV) – USMILE project
* Helmholtz-Zentrum für Umweltforschung (UFZ)

## 📂 Original dataset

| Version                  | DOI                    | Notes                                             |
| ------------------------ | ---------------------- | ------------------------------------------------- |
| 1.0.0                    | [10.25532/OPARA-703](https://doi.org/10.25532/OPARA-703) | Zarr minicubes (2.3 TB)                       |


## 🌮 Taco dataset

Each sample folder contains:

| File            | Format  | Shape             | Description             |
| --------------- | ------- | ----------------- | ----------------------- |
| `bands_rgb.mp4` | H.265   | T × 128 × 128 × 3 | B04-B03-B02, 12-bit     |
| `bands_ir.mp4`  | H.265   | T × 128 × 128 × 4 | B8A-B05-B06-B07, 12-bit |
| `masks.mp4`     | FFV1    | T × 128 × 128 × 3 | cloud, SCL, validity    |
| `era5.zarr`     | zstd    | T × 13 vars       | ERA5-Land point series  |
| `dem.tif`       | GeoTIFF | 85×85             | Copernicus DEM 30 m     |
| `landcover.tif` | GeoTIFF | 85×85             | ESA-CCI LC 300 m        |

All videos use **preset = medium, tune = psnr, qp = 1-5** yielding ≈ 56 dB PSNR per channel.


## ⚡ Reproducible Example

<a target="_blank" href="https://colab.research.google.com/drive/1T9sT2Q0MkoplClRzRWJ22gB2rNXthcjT?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("isp-uv-es:deepextremecubes-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: **sentinel2msi**, **era5-land**, **copernicus-dem**

## 🎯 Task

Intended tasks: **impact-mapping**, **forecasting**, **self-supervised learning**

## 📂 Original Data Repository

Raw data: [10.25532/OPARA-703](https://doi.org/10.25532/OPARA-703)

## 💬 Discussion

Join the conversation: [https://huggingface.co/datasets/tacofoundation/DeepExtremeCubes-video/discussions](https://huggingface.co/datasets/tacofoundation/DeepExtremeCubes-video/discussions)

## 🔀 Split Strategy

All train.

## 📚 Scientific Publications


### Publication 01

- **DOI**: [10.48550/arXiv.2410.01770](https://doi.org/10.48550/arXiv.2410.01770)
- **Summary**: 
DeepExtremeCubes (~40,000 Sentinel-2 minicubes from 2016–2022 with extreme-event labels, meteorology, vegetation cover, and topography) powered a convLSTM achieving R² = 0.9055 for predicting reflectance and NDVI. Explainable AI on October 2020 South America heatwave–drought versus October 2019 revealed a shift from temperature and pressure predictors to evaporation and distinct latent heat anomalies
- **BibTeX Citation**:
```bibtex
@article{pellicer2024explainable,
	title        = {Explainable Earth Surface Forecasting under Extreme Events},
	author       = {Pellicer-Valero, Oscar J and Fern{\'a}ndez-Torres, Miguel-{\'A}ngel and Ji, Chaonan and Mahecha, Miguel D and Camps-Valls, Gustau},
	year         = 2024,
	journal      = {arXiv preprint arXiv:2410.01770}
}
```

### Publication 02
- **DOI**: [10.1038/s41597-025-04447-5](https://doi.org/10.1038/s41597-025-04447-5)
- **Summary**: 
DeepExtremeCubes is a global database of over 40,000 2.5 × 2.5 km minicubes combining Sentinel-2 L2A imagery, analysis-ready ERA5-Land data and extreme-event flags, plus land cover and topography (2016–2022). Designed to improve accessibility, reproducibility and support machine learning forecasting of ecosystem responses to compound heatwave and drought extremes, focusing on persistent natural vegetation.

- **BibTeX Citation**:

```bibtex
@article{ji2025deepextremecubes,
	title        = {DeepExtremeCubes: Earth system spatio-temporal data for assessing compound heatwave and drought impacts},
	author       = {Ji, Chaonan and Fincke, Tonio and Benson, Vitus and Camps-Valls, Gustau and Fern{\'a}ndez-Torres, Miguel-{\'A}ngel and Gans, Fabian and Kraemer, Guido and Martinuzzi, Francesco and Montero, David and Mora, Karin and others},
	year         = 2025,
	journal      = {Scientific Data},
	publisher    = {Nature Publishing Group UK London},
	volume       = 12,
	number       = 1,
	pages        = 149
}
```

## 🤝 Data Providers

| Name                        | Role        | URL                                                                      |
| --------------------------- | ----------- | ------------------------------------------------------------------------ |
| European Space Agency (ESA) | producer    | [SENTINEL ESA](https://sentinel.esa.int/)                   |
| ECMWF                       | producer    | [CLIMATE COPERNICUS](https://cds.climate.copernicus.eu/) |
| Copernicus DEM              | contributor | [LAND COPERNICUS](https://land.copernicus.eu/)               |                |

## 🧑‍🔬 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)         |