Data Acquisition and Processing for GeoAI Models to Support Sustainable Agricultural Practices García Pereira, Agustín Ojo, Adegboyega Curry, Edward Porwol, Lukasz 2020-01-04T07:19:56Z 2020-01-04T07:19:56Z 2020-01-07
dc.description.abstract 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.
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2020.115
dc.identifier.isbn 978-0-9981331-3-3
dc.language.iso eng
dc.relation.ispartof Proceedings of the 53rd Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.subject Analytics and Decision Support for Green IS and Sustainability Applications
dc.subject artificial intelligence
dc.subject crop rotations
dc.subject data
dc.subject geoai
dc.subject geospatial
dc.title Data Acquisition and Processing for GeoAI Models to Support Sustainable Agricultural Practices
dc.type Conference Paper
dc.type.dcmi Text
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