Ornela Nanushi
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Research associate
National Observatory of Athens |
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Ornela Nanushi received her B.Sc. diploma from the National and Kapodistrian university of Athens, department of Mathematics. During her studies, she was also implicated with many volunteering and tutoring activities. She was an active member of the student association MathAid Greece, where she used to provide free online maths courses for students in financial difficulty. Moreover, she was a mentor for 2 years for an educative robotics children’s team, with whom she achieved their participation in international competitions. She also spent a semester abroad as part of the Erasmus student exchange programme, at the University of Strasbourg, France at the department of Mathematics and Computer Science. She now holds an M.Sc. in Data Science and Engineering, from the university of Rouen Normandy. As part of her master studies, she was a research intern at the French Institute of Applied Sciences (INSA Rouen, Normandy). Currently, at the National Observatory of Athens under the title of “Research associate”, she is using earth observation data and machine learning in support of resilient agriculture. |
Workshop: Earth observation and machine learning for agroecological applications
October 26, 2022; 9:00am - 10:00am
October 26, 2022; 9:00am - 10:00am
The usage of machine learning (ML) has been growing exponentially. Its significant power in generalization and the large amount of available data make machine learning indispensable. In parallel, humanity is focused more than ever on space exploration, developing cutting-edge Earth Observation (EO) technology. Have you ever wondered how these two can be combined?
One domain that can be greatly benefited from this coalition is agriculture. With climate change and population rise, maintaining natural ecosystems while enhancing agricultural productivity and supporting farmers is of primary importance. In this sense, ML and EO technologies are the key enablers in developing actionable recommendations for farmers and policymakers to achieve resilient agriculture. In this workshop, we discuss the usage of ML for EO-related applications, focusing on agriculture and ecosystem services. We will present two applications of how ML bridges the gap between scientific knowledge and actionable advice for farmers and policymakers. The first application will consist of a predictive ML model related to the occurrence of pests in cotton fields. The second application will showcase the combination of a geographical model and a ML algorithm to identify the local-specific contribution of agricultural management to ecosystem services. For both applications, there will be live demonstrations using Python and R. By the end of this workshop, we hope you will be acquainted with establishing the link between machine learning, earth observation and sustainable agriculture. Wishing you a fruitful exploration of this field having provided you with the necessary tools to start your journey!
One domain that can be greatly benefited from this coalition is agriculture. With climate change and population rise, maintaining natural ecosystems while enhancing agricultural productivity and supporting farmers is of primary importance. In this sense, ML and EO technologies are the key enablers in developing actionable recommendations for farmers and policymakers to achieve resilient agriculture. In this workshop, we discuss the usage of ML for EO-related applications, focusing on agriculture and ecosystem services. We will present two applications of how ML bridges the gap between scientific knowledge and actionable advice for farmers and policymakers. The first application will consist of a predictive ML model related to the occurrence of pests in cotton fields. The second application will showcase the combination of a geographical model and a ML algorithm to identify the local-specific contribution of agricultural management to ecosystem services. For both applications, there will be live demonstrations using Python and R. By the end of this workshop, we hope you will be acquainted with establishing the link between machine learning, earth observation and sustainable agriculture. Wishing you a fruitful exploration of this field having provided you with the necessary tools to start your journey!
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