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SatelliteSkill5 —An Augmented Reality Educational Experience Teaching Remote Sensing through the UN Sustainable Development Goals.

Authors :
McNerney, Eimear
Faull, Jonathan
Brown, Sasha
McNerney, Lorraine
Foley, Ronan
Lonergan, James
Rickard, Angela
Doganca Kucuk, Zerrin
Behan, Avril
Essel, Bernard
Mensah, Isaac Obour
Castillo Campo, Yeray
Cullen, Helen
Ffrench, Jack
Abernethy, Rachel
Cleary, Patricia
Byrne, Aengus
Cahalane, Conor
Source :
Remote Sensing. Dec2023, Vol. 15 Issue 23, p5480. 18p.
Publication Year :
2023

Abstract

Advances in visualisation techniques provide new ways for us to explore how we introduce complex topics like remote sensing to non-specialist audiences. Taking inspiration from the popularity of augmented reality (AR) apps, a free, mobile digital AR app titled SatelliteSkill5, has been developed for both Androids and iPhones in Unity AR. SatelliteSkill5 helps users conceptualise remote sensing (RS) theory and technology by showcasing the potential of datasets such as multispectral images, SAR backscatter, drone orthophotography, and bathymetric LIDAR for tackling real-world challenges, with examples tackling many of the United Nations' Sustainable Development Goals (SDGs) as the focus. Leveraging tried and tested pedagogic practices such as active learning, game-based learning, and targeting cross-curricular topics, SatelliteSkill5 introduces users to many of the fundamental geospatial data themes identified by the UN as essential for meeting the SDGs, imparting users with a familiarity of concepts such as land cover, elevation, land parcels, bathymetry, and soil. The SatelliteSkill5 app was piloted in 12 Irish schools during 2021 and 2022 and with 861 students ranging from 12 to 18 years old. This research shows that both students and teachers value learning in an easy-to-use AR environment and that SDGs help users to better understand complex remote sensing theory. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20724292
Volume :
15
Issue :
23
Database :
Academic Search Index
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
174111938
Full Text :
https://doi.org/10.3390/rs15235480