Pereira, Agustín García and Porwol, Lukasz and Ojo, Adegboyega and Curry, Edward (2021) Exploiting the Temporal Dimension of Remotely Sensed Imagery with Deep Learning Models. In: Proceedings of the 54th Hawaii International Conference on System Sciences, January 2021.

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Abstract

The rapid rise of artificial intelligence and the increasing availability of open Earth Observation (EO) data present new opportunities to address important global problems such as the proliferation of agricultural systems which endanger ecological sustainability. Despite the plethora of satellite images describing a given location on earth every year, very few deep learning-based solutions have harnessed the temporal and sequential dynamics of land use to map agricultural practices. This paper compares different approaches to classify agricultural land use exploiting the temporal and spectral dimensions of EO data. The results show greater efficiency of the presented deep learning-based algorithms compared to state-of-the-art approaches when mapping agricultural classes.

Item Type:	Conference or Workshop Item (Paper)
Keywords:	Exploiting; Temporal Dimension; Remotely Sensed Imagery; Deep Learning Models;
Academic Unit:	Faculty of Social Sciences > Research Institutes > Innovation Value Institute, IVI Faculty of Social Sciences > School of Business
Item ID:	15784
Depositing User:	Adegboyega Ojo
Date Deposited:	06 Apr 2022 09:17
Journal or Publication Title:	Proceedings of the 54th Hawaii International Conference on System Sciences
Refereed:	Yes
URI:
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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