Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/63854

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

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Title:Data Acquisition and Processing for GeoAI Models to Support Sustainable Agricultural Practices
Authors:García Pereira, Agustín
Ojo, Adegboyega
Curry, Edward
Porwol, Lukasz
Keywords:Analytics and Decision Support for Green IS and Sustainability Applications
artificial intelligence
crop rotations
data
geoai
show 1 moregeospatial
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Date Issued:07 Jan 2020
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.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/63854
ISBN:978-0-9981331-3-3
DOI:10.24251/HICSS.2020.115
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Analytics and Decision Support for Green IS and Sustainability Applications


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