The Big Data accumulating from remote sensing technology in ground, aerial, or satellite-based Earth Observation (EO) has radically changed how the state of our planet is monitored.

The ever-growing availability of high-resolution remote sensing data increasingly confronts researchers and data science engineers with machine learning challenges posed by characteristic heterogeneity and correlation structures in these data.

Massive data access

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Massive data processing

for e.g. climate indicators generation (how to move from large amount of global environmental data to very focused and sharp information – the CCKP data creation process)

ML/DL/AI intro and applications

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Big data

Nowadays the world is accumulating a large amount of information, the big data era is also happening in the earth observation field. Indeed in the context of the Copernicus programme with the opening of satellite images, plus the growing of new space companies, the number of satellite images is increasing really fast. Moreover than the number of images, the quality and precision of those are also increasing. In the same way machine learning has seen some important improvements in the last decade, revolutionising the state of the art in computer vision and big data analysis.

Technological improvement

This overall technological improvement is a good opportunity for earth observation, indeed the link between AI and EO is not only scientifically interesting but became a necessity. Machine learning models permit to quickly analyse images (classification, segmentation, object detection), but it can also offers the possibility to enhance the spatial resolution (super resolution) or even trying to predict what could happen in the future (LSTM). The machine is a powerful tool necessary to support the geo-analyst to manage the large amount of information he is facing.

Quantum computing

Quantum theory is rapidly evolving in different advanced technologies with big impact to space applications. The quantum sensing and imaging enable new measurements and observation concepts of physical quantities, achieving higher levels of accuracy sometimes beyond the classical limits. Therefore quantum simulations can boost the forecast of climate and our planet phenomena, whereas quantum computing (e.g. quantum algorithms, quantum sensing, etc.) can solve challenges of the Big EO Data and AI solutions.

Related projects


AI and ML are fundamental tools identify hidden information from EO data. Within EO4YEMEN we have developed an advanced AI-based super resolution tool, a ML-based urban classification tool and an object-based classification tool to identify forma and informal settlements in conflict areas.

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⸺ SR4C3

The “Super Resolution for Climate Crisis Context – SR4C3” aims at bringing innovation to the climate crisis sector by enhancing the remote sensing based technological tools through the application of Artificial Intelligence (AI) algorithms on security domain challenges. The integration of Earth Observation (EO) satellite data in the usage of new Super Resolution (SR) AI-based applications is particularly crucial for better information gathering and assessments in conflict or crisis affected areas otherwise largely inaccessible, to effectively contribute to global stability and peace.

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