Data Acquisition and Processing for GeoAI Models to Support Sustainable Agricultural Practices

García Pereira, Agustín
Ojo, Adegboyega
Curry, Edward
Porwol, Lukasz
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There are growing opportunities to leverage new technologies and data sources to address global problems related to sustainability, climate change, and biodiversity loss. The emerging discipline of GeoAI resulting from the convergence of AI and Geospatial science (Geo-AI) is enabling the possibility to harness the increasingly available open Earth Observation data collected from different constellations of satellites and sensors with high spatial, spectral and temporal resolutions. However, transforming these raw data into high-quality datasets that could be used for training AI and specifically deep learning models are technically challenging. This paper describes the process and results of synthesizing labelled-datasets that could be used for training AI (specifically Convolutional Neural Networks) models for determining agricultural land use pattern to support decisions for sustainable farming. In our opinion, this work is a significant step forward in addressing the paucity of usable datasets for developing scalable GeoAI models for sustainable agriculture.
Analytics and Decision Support for Green IS and Sustainability Applications, artificial intelligence, crop rotations, data, geoai, geospatial
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