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Big streams of Earth images from satellites or other platforms (e.g., dronesand mobile phones) are becoming increasingly available at low or no cost andwith enhanced spatial and temporal resolution. This thesis recognizes theunprecedented opportunities offered by the high quality and open access Earthobservation data of our times and introduces novel machine learning and bigdata methods to properly exploit them towards developing applications forsustainable and resilient agriculture. The thesis addresses three distinctthematic areas, i.e., the monitoring of the Common Agricultural Policy (CAP),the monitoring of food security and applications for smart and resilientagriculture. The methodological innovations of the developments related to thethree thematic areas address the following issues: i) the processing of bigEarth Observation (EO) data, ii) the scarcity of annotated data for machinelearning model training and iii) the gap between machine learning outputs andactionable advice.  This thesis demonstrated how big data technologies such as data cubes,distributed learning, linked open data and semantic enrichment can be used toexploit the data deluge and extract knowledge to address real user needs.Furthermore, this thesis argues for the importance of semi-supervised andunsupervised machine learning models that circumvent the ever-present challengeof scarce annotations and thus allow for model generalization in space andtime. Specifically, it is shown how merely few ground truth data are needed togenerate high quality crop type maps and crop phenology estimations. Finally,this thesis argues there is considerable distance in value between modelinferences and decision making in real-world scenarios and thereby showcasesthe power of causal and interpretable machine learning in bridging this gap.

Big Earth Data and Machine Learning for Sustainable and Resilient Agriculture