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2025
Tsoumas, I., Sitokonstantinou, V., Giannarakis, G., Lampiri, E., Athanassiou, C., Camps-Valls, G., Kontoes, C. and Athanasiadis, I.N., 2025. Leveraging causality and explainability in digital agriculture. Environmental Data Science, 4, p.e23. Link to pdf
Cohrs, K.-H., Diaz, E., Sitokonstantinou, V., Varando, G., & Camps-Valls, G. (2025). Large Language Models for Causal Hypothesis Generation in Science. In Machine Learning: Science and Technology. Link to pdf
2024
Sitokonstantinou, V., Porras, E. D. S., Bautista, J. C., Piles, M., Athanasiadis, I., Kerner, H., Martini, G., Sweet, L. B., Tsoumas, I., Zscheischler, J., & others. (2024). Causal machine learning for sustainable agroecosystems. Preprint. Link to pdf
Cerdà-Bautista, J., Tárraga, J. M., Sitokonstantinou, V., & Camps-Valls, G. (2024, July). Assessing the Causal Impact of Humanitarian Aid on Food Security. In IGARSS 2024-2024 IEEE International Geoscience and Remote Sensing Symposium (pp. 1546-1552). IEEE. Link to pdf
Tàrraga, J. M., Sevillano-Marco, E., Muñoz-Marí, J., Piles, M., Sitokonstantinou, V., Ronco, M., Miranda, M. T., Cerdà, J., & Camps-Valls, G. (2024). Causal discovery reveals complex patterns of drought-induced displacement. iScience, 27(9). Elsevier. Link to pdf
Cohrs, K.-H., Varando, G., Diaz, E., Sitokonstantinou, V., & Camps-Valls, G. (2024). Large Language Models for Constrained-Based Causal Discovery. AAAI Workshop on Are Large Language Models Simply Causal Parrots? Link to pdf
Koukos, A., Jo, H.-W., Sitokonstantinou, V., Tsoumas, I., Kontoes, C., & Lee, W.-K. (2024). Towards Global Crop Maps with Transfer Learning. IGARSS 2024-2024 IEEE International Geoscience and Remote Sensing Symposium, 1540--1545. IEEE. Link to pdf
Tsardanidis, I., Koukos, A., Sitokonstantinou, V., Drivas, T., & Kontoes, C. (2024). Cloud gap-filling with deep learning for improved grassland monitoring. Computers and Electronics in Agriculture. Link to pdf
2023
Tsoumas, I.*, Giannarakis, G.*, Sitokonstantinou, V., Koukos, A., Loka, D., Bartsotas, N., Kontoes, C., & Athanasiadis, I. (2023). Evaluating Digital Agriculture Recommendations with Causal Inference. Thirty-Seventh AAAI Conference on Artificial Intelligence. Link to pdf.
Jo, H.-W., Park, E., Sitokonstantinou, V., Kim, J., Lee, S., Koukos, A., & Lee, W.-K. (2023). Recurrent U-Net based dynamic paddy rice mapping in South Korea with enhanced data compatibility to support agricultural decision making. GIScience & Remote Sensing, 60(1), 2206539. Taylor & Francis. Link to pdf
Sitokonstantinou, V., Koukos, A., Tsoumas, I., Bartsotas, N. S., Kontoes, C., & Karathanassi, V. (2023). Fuzzy clustering for the within-season estimation of cotton phenology. PloS ONE. Link to pdf
Tsoumas, I.*, Sitokonstantinou, V.*, Giannarakis, G., Lampiri, E., Athanassiou, C., Camps-Valls, G., Kontoes, C., & Athanasiadis, I. (2023). Causality and Explainability for Trustworthy Integrated Pest Management. NeurIPS Workshop Climate Change AI. Link to pdf
2022
Giannarakis, G., Sitokonstantinou, V., Lorilla, R. S., & Kontoes, C. (2022). Towards assessing agricultural land suitability with causal machine learning. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1442--1452. Link to pdf
Ioannidou, M., Koukos, A., Sitokonstantinou, V., Papoutsis, I., & Kontoes, C. (2022). Assessing the added value of Sentinel-1 PolSAR data for crop classification. Remote Sensing, 14(22), 5739. MDPI. Link to pdf
Jo, H.-W., Koukos, A., Sitokonstantinou, V., Lee, W.-K., & Kontoes, C. (2022). Towards Global Crop Maps with Transfer Learning. NeurIPS Workshop Climate Change AI. Link to pdf
Giannarakis, G., Sitokonstantinou, V., Lorilla, R. S., & Kontoes, C. (2022). Personalizing sustainable agriculture with causal machine learning. NeurIPS Workshop Climate Change AI. Link to pdf
Sitokonstantinou, V., Koukos, A., Drivas, T., Kontoes, C., & Karathanassi, V. (2022). DataCAP: A Satellite Datacube and Crowdsourced Street-level Images for the Monitoring of the Common Agricultural Policy. 28th International Conference on Multimedia Modeling, 13142, Lecture--Notes. Link to pdf
Nanushi, O.*, Sitokonstantinou, V.*, Tsoumas, I., & Kontoes, C. (2022). Pest presence prediction using interpretable machine learning. 2022 IEEE 14th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP), 1--5. IEEE. Link to pdf
2021
Sitokonstantinou, V., Koukos, A., Drivas, T., Kontoes, C., Papoutsis, I., & Karathanassi, V. (2021). A Scalable Machine Learning Pipeline for Paddy Rice Classification Using Multi-Temporal Sentinel Data. Remote Sensing, 13(9), 1769. Multidisciplinary Digital Publishing Institute. Link to pdf
2020
Rousi, M.*, Sitokonstantinou, V.*, Meditskos, G., Papoutsis, I., Gialampoukidis, I., Koukos, A., Karathanassi, V., Drivas, T., Vrochidis, S., & Kontoes, C. (2020). Semantically enriched crop type classification and linked earth observation data to support the common agricultural policy monitoring. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 529--552. IEEE. Link to pdf
Sitokonstantinou, V., Koutroumpas, A., Drivas, T., Koukos, A., Karathanassi, V., Kontoes, H., & Papoutsis, I. (2020). A Sentinel based agriculture monitoring scheme for the control of the CAP and food security. Eighth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2020), 11524, 48--59. SPIE. Link to pdf
2019
Sitokonstantinou, V., Drivas, T., Koukos, A., Papoutsis, I., & Kontoes, C. (2019). Scalable distributed Random Forest for paddy rice mapping. 40th Asian Conference on Remote Sensing (ACRS 2019), 2, 836--845. Link to pdf
2018
Sitokonstantinou, V., Papoutsis, I., Kontoes, C., Lafarga Arnal, A., Armesto Andrés, A. P., & Garraza Zurbano, J. A. (2018). Scalable Parcel-Based Crop Identification Scheme Using Sentinel-2 Data Time-Series for the Monitoring of the Common Agricultural Policy. Remote Sensing - An open-access journal. MDPI. Link to pdf
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