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Semantically Enriched Crop Type Classification and Linked Earth Observation Data to Support the Common Agricultural Policy Monitoring., , , , , , , , , and 1 other author(s). IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., (2021)Causality and Explainability for Trustworthy Integrated Pest Management., , , , , , , and . CoRR, (2023)Fuzzy clustering for the within-season estimation of cotton phenology., , , , , and . CoRR, (2022)A Scalable Machine Learning Pipeline for Paddy Rice Classification Using Multi-Temporal Sentinel Data., , , , , and . Remote. Sens., 13 (9): 1769 (2021)Pest Presence Prediction Using Interpretable Machine Learning., , , and . IVMSP, page 1-5. IEEE, (2022)Cloud gap-filling with deep learning for improved grassland monitoring., , , , and . CoRR, (2024)Towards Space-to-Ground Data Availability for Agriculture Monitoring., , , and . IVMSP, page 1-5. IEEE, (2022)Scalable Parcel-Based Crop Identification Scheme Using Sentinel-2 Data Time-Series for the Monitoring of the Common Agricultural Policy., , , , , and . Remote Sensing, 10 (6): 911 (2018)Evaluating Digital Tools for Sustainable Agriculture using Causal Inference., , , , , , , and . CoRR, (2022)Towards assessing agricultural land suitability with causal machine learning., , , and . CVPR Workshops, page 1441-1451. IEEE, (2022)