Roxanne Suzette Lorilla
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Post-doctoral researcher
National Observatory of Athens |
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Roxanne Suzette Lorilla received her Bachelor’s degree in Environmental Technology and her Master’s degree in Geoinformatics in 2014 and 2016, respectively. She holds a Ph.D. in Geography and Spatial Analysis from the Harokopio University of Athens. Her doctoral research included mapping and assessing ecosystem services and revealing the socio-ecological determinants of the supply and demand of ecosystem services. Currently, Roxanne is undertaking the technical implementation and project management of Research Projects of the BEYOND Centre of Excellence (National Observatory of Athens), to which she utilizes earth observation data and machine learning techniques to identify possible drivers of ecosystem services and ensure resilience of agricultural landscapes. Additional research activities include her participation to the undertaking of the IPBES (Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services) Nexus Assessment on the interlinkages among biodiversity, water, food and health, and the co-leading of the Τhematic Working Group on Ecosystem Services Indicators of the ESP (Ecosystem Services Partnership). |
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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