García Pereira, Agustín and Ojo, Adegboyega and Curry, Edward and Porwol, Lukasz (2020) Data Acquisition and Processing for GeoAI Models to Support Sustainable Agricultural Practices. In: Proceedings of the 53rd Hawaii International Conference on System Sciences. HICSS, pp. 922-931. ISBN 978-0-9981331-3-3

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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.

Item Type:	Book Section
Keywords:	Data Acquisition; Processing; GeoAI Models; Support Sustainable; Agricultural Practices;
Academic Unit:	Faculty of Social Sciences > Research Institutes > Innovation Value Institute, IVI Faculty of Social Sciences > School of Business
Item ID:	15788
Identification Number:	https://doi.org/10.24251/hicss.2020.115
Depositing User:	Adegboyega Ojo
Date Deposited:	06 Apr 2022 11:04
Publisher:	HICSS
Refereed:	Yes
URI:	Organisation
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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