Analytics Made Easy - StatCounter

Since launching in 2017, Microsoft has awarded 236 AI for Earth Innovation Grants to individuals and organisations committed to using AI for the good of our planet. In the Middle East and Africa, the company has awarded 34 grants to recipients across 12 countries, empowering people like Ketty Adoch in Uganda, who used the grant to develop an AI solution that detects, quantifies and monitors land cover change.

In every corner of the world, researchers and organisations face unprecedented challenges – to develop solutions that address climate change and a catastrophic loss of biodiversity while sustainably feeding a growing population and protecting water supplies.

To bridge this gap, Microsoft is empowering people and organisations with AI and cloud tools to solve global environmental challenges. Through our AI for Earth programme, individuals and organisations are now using AI to collect, process and analyse data at a scale and speed previously unimaginable. By turning information into insight, they can prevent and even predict environmental threats.

To date, we’ve awarded 34 grants to projects with impact in 12 countries across the Middle East and Africa – a community we’re committed to keep growing.

Microsoft’s AI for Earth programme awards grants to support projects that change the way we monitor model and ultimately manage Earth’s natural systems.

Now, through our partnership with the Leonardo DiCaprio Foundation and the National Geographic Society, we’re awarding three new grants to fellow pioneers looking to help us build a more sustainable future.


What are other AI for Earth grantees doing?

One AI for Earth grantee is leveraging AI to help farmers yield more crops with less water. When it comes to this kind of water management, precision is key.

One way to know how much water crops really need is by measuring the rate at which water evaporates from soil and plant surfaces.

Torsten Bondo and Radoslaw Guzinski, both from the DHI Group, are exploring ways to generate fi eld-level ET measurements using machine learning and satellite imagery.

They’re developing an open-source algorithm that can merge data from optical and thermal satellites, as well as meteorological data to determine the right amount of water for effective irrigation.