This dataset follows the TACO specification.

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 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 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

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	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

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"]

🛰️ 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

💬 Discussion

Join the conversation: https://huggingface.co/datasets/tacofoundation/DeepExtremeCubes-video/discussions

🔀 Split Strategy

All train.

📚 Scientific Publications

Publication 01

• DOI: 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:

@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

• 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:

@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
ECMWF	producer	CLIMATE COPERNICUS
Copernicus DEM	contributor	LAND COPERNICUS

🧑‍🔬 Curators

Name	Organization	URL
Oscar J. Pellicer-Valero	Image Signal Processing (ISP)	Google Scholar
Cesar Aybar	Image Signal Processing (ISP)	Google Scholar
Julio Contreras	Image Signal Processing (ISP)	GitHub