Annotated InSAR datasets for volcanic unrest detection

These datasets have been published in the context of the DeepCube Use Case on global volcanic unrest detection and alerting led by the National Observatory of Athens.

To create the C1 dataset we downloaded and manually annotated InSAR products from the COMET-LiCS Sentinel-1 InSAR portal ( The S1 dataset has been provided by the University of Bristol and is part of the work of Anantrasirichai et al (

To download the datasets please visit

Cite our work: N. I. Bountos, I. Papoutsis, D. Michail and N. Anantrasirichai, “Self-Supervised Contrastive Learning for Volcanic Unrest Detection,” in IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5, 2022, Art no. 3003905, doi: 10.1109/LGRS.2021.3104506.


Sample fromC the C1 dataset
Sample from the S1 dataset

Data cube for drought forecasting in Africa

This is a dataset of minicubes suitable for Earth Surface Forecasting, created by the DeepCube research team of the Max Planck Institute for Biogeochemistry. The dataset is focused on Africa and the task is centered around drought impact forecasting.

To learn more about the Africa minicubes please visit

Data cube for the wildfire research community

Image: Plots of land surface temperature for a series of days for the whole Greece region.

This dataset has been created by the DeepCube research team of the National Observatory of Athens and is meant to be used to develop models for next day fire hazard forecasting in Greece. 

The dataset includes dynamic variables, such as previous day Leaf Area Index, evapotransiration, Land Surface Temperature, meteorological data, fire variables and Fire Weather Index, resampled at daily temporal resolution and 1km spatial resolution. It also includes static variables, such as roads density, population density and topography layers.

To download and directly access the data cube please visit

To import and analyse the data within the data cube, you can use the Jupyter Notebook we have created here.

Data cube to calculate the environmental impact of tourism in Brazil

Image: Plots of 4 random variables at the same date. Specifically, O3/CH4 Copernicus atmosphere composition forecasting and UTCI/MRT thermal index from Copernicus Climate Change over the Sao Luis’ region in Brazil.

Murmuration, the greentech committed to sustainable tourism, has developed this datacube to calculate the impact of tourism activity in certain areas of Brazil.

The aim of the dataset is to use models to isolate the impact of a travel package offered by a tourism stakeholder, so that the virtual extra cost of a tourist on the local environment can be calculated.

The datacube includes dynamic variables such as the land surface temperature, the soil moisture condition, the Normalized Difference Vegetation Index, the atmospheric composition through different variables (carbon monoxide, nitrogen dioxide, zone, sulphure dioxide, etc), the thermal comfort index of the Tourism Sustainable Development Index, as well as other static variables such as topographic layers or land cover maps.

To access and download the datacube please visit

Access the data cubes through Copernicus ONDA DIAS

To facilitate the exploitation of the data cubes produced within this task, we have uploaded the datasets to the Swift Object Store on ONDA DIAS and made them publicly available. Interested stakeholders can access the data, inspect the metadata and parts of the datasets without having to download the whole data cube. The access URLs for the use case data cubes are the following:

For drought forecasting in Africa:

For wildfire hazard forecasting:

For sustainable tourism:

Image: Code example to open a data data cube from within python.

This opens the dataset and lets the user access all dataset and variable metadata, without downloading the actual data. Subsets can then be selected and distributed computations can be directly performed on the dataset using xarray and dask. Please note that the links provided above do not work as direct download links but serve as an entry point for exploration from within a programming environment like python or Julia.

For demonstration purposes we have prepared some Jupyter notebooks on how to access the data cubes, which can be found in