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GROW Observatory

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The aim of the GROW project was to empower farmer and food-growing communities to improve the quality of soils using sensors, an app and satellite data.

LANDSUPPORT – Development of integrated web-based land decision support system aiming towards the implementation of policies for agriculture and environment

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The objective of LANDSUPPORT is the construction of a free, web-based smart geoSpatial Decision Support System (S-DSS), consisting of a powerful set of tools aimed at supporting sustainable agriculture/forestry, evaluating trade-offs between land uses (including spatial planning) and contributing to the implementation of land-related policies.

QuantiFarm – Assessing the impact of digital technology solutions in agriculture in real-life conditions

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The overarching aim of the QuantiFarm project is to support the further deployment of digital technologies in agriculture (DATs) as key enablers for enhancing the sustainability performance and competitiveness of the agricultural sector. To achieve this goal, QuantiFarm aims to establish a comprehensive assessment framework for the independent qualitative and quantitative assessment of the multiple costs and benefits of DATs, as well as of their sustainability impact.

LANDSENSE – A citizen observatory and innovation marketplace for land use and land cover monitoring

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LandSense is a modern citizen observatory for Land Use & Land Cover (LULC) monitoring, connecting citizens with Earth Observation (EO) data to transform current approaches to environmental decision making across urban greenspaces, agricultural management and biodiversity/habitat threats. The project involved demonstration pilots in which citizens used their own devices to supplement the existing monitoring through interactive reporting, gaming applications and mapathons. Some of the LandSense pilots focused on in-situ observations using mobile apps, whereas other pilots were centered around satellite-image interpretations during dedicated mapathons. Particular attention was given to quality assurance of citizen observations and defining methods for integrating citizen science data with authoritative LULC data.