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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.
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Authors (4)
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Agustin Garcia Pereira Master Student
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Curry Edward Dr
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Lukasz Porwol Dr
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- Accepted or Published Version
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Details
- Category:
- Conference activity
- Type:
- publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
- Language:
- English
- Publication year:
- 2020
- Bibliographic description:
- Pereira A., Ojo A., Edward C., Porwol L.: Data Acquisition and Processing for GeoAI Models to Support Sustainable Agricultural Practices// / : , 2020,
- DOI:
- Digital Object Identifier (open in new tab) 10.24251/hicss.2020.115
- Verified by:
- Gdańsk University of Technology
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