Big Earth Data and Machine Learning for Sustainable and Resilient Agriculture

• URL: http://arxiv.org/abs/2211.12584v1
• Date: Tue, 22 Nov 2022 20:58:54 GMT
• Title: Big Earth Data and Machine Learning for Sustainable and Resilient Agriculture
• Authors: Vasileios Sitokonstantinou
• Abstract summary: This thesis recognizes the unprecedented opportunities offered by the high quality and open access Earth observation data of our times. It introduces novel machine learning and big data methods to properly exploit them towards developing applications for sustainable and resilient agriculture.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Big streams of Earth images from satellites or other platforms (e.g., drones and mobile phones) are becoming increasingly available at low or no cost and with enhanced spatial and temporal resolution. This thesis recognizes the unprecedented opportunities offered by the high quality and open access Earth observation data of our times and introduces novel machine learning and big data methods to properly exploit them towards developing applications for sustainable and resilient agriculture. The thesis addresses three distinct thematic areas, i.e., the monitoring of the Common Agricultural Policy (CAP), the monitoring of food security and applications for smart and resilient agriculture. The methodological innovations of the developments related to the three thematic areas address the following issues: i) the processing of big Earth Observation (EO) data, ii) the scarcity of annotated data for machine learning model training and iii) the gap between machine learning outputs and actionable advice. This thesis demonstrated how big data technologies such as data cubes, distributed learning, linked open data and semantic enrichment can be used to exploit the data deluge and extract knowledge to address real user needs. Furthermore, this thesis argues for the importance of semi-supervised and unsupervised machine learning models that circumvent the ever-present challenge of scarce annotations and thus allow for model generalization in space and time. Specifically, it is shown how merely few ground truth data are needed to generate high quality crop type maps and crop phenology estimations. Finally, this thesis argues there is considerable distance in value between model inferences and decision making in real-world scenarios and thereby showcases the power of causal and interpretable machine learning in bridging this gap.

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