Consolidator Grant to develop models based on remote sensing data and machine learning
The rapid growth of the world’s population has increased the demand for intensive agriculture. Unfortunately, this often comes with negative environmental impacts. This is why more and more people are looking for new ways of sustainable agriculture, where the environmental impact is reduced as yields increase. Nature-based solutions, such as wetlands and riparian buffer strips along watercourses, can effectively reduce nutrient (nitrogen and phosphorus) runoff from agricultural catchments. However, it is neither economically viable nor, in most cases, naturally feasible to establish them throughout the landscape. It is therefore important to identify priority areas in the landscape when planning nature-based solutions, making smart use of spatial data.
The team of Evelyn Uuemaa, Professor of Geoinformatics at the University of Tartu, uses geoinformatics methods to test existing spatial data processing solutions and create new ones. According to Uuemaa, the amount of spatial data from satellites has exploded in the last 10–15 years. “Yet this is largely an untapped resource, as the spatial data processing capabilities and the machine learning skills that can be applied to them have not caught up with the large volumes of data. So, this data has largely remained unused in decision-making processes,” explained Uuemaa.
More information regarding the project
Funding agency: European Research Council
Principal investigator: Evelyn Uuemaa, 1.01.2024−31.12.2028
