• We are one of the winners of the myEUspace competition of EUSPA - EU Agency for the Space Programme. Our idea (DeGenS) combines blockchain, AI, EO and NFT to incentivize crowdsourcing, streamline space-to-ground data availability and empower AI4EO research (Feb. 2023).
• Best proposal award in NeurIPS 2022 Workshop: Tackling Climate Change with Machine Learning -> Paper: Personalizing Sustainable Agriculture with Causal Machine Learning (Dec. 2022)
• Paper accepted in AAAI-23 Special Track on AI for Social Impact -> Paper: Evaluating Digital Agriculture Recommendations with Causal Inference (Nov. 2022)

AgriHUB is a research group within the Beyond Center of Earth Observation Research of the National Observatory of Athens, collaborating with the Image Processing Laboratory of the University of Valencia. We conduct research in the domain of Big Earth data and Artificial Intelligence trying to understand agro-ecosystems and develop applications that support sustainable and resilient agriculture. Over the last five years, our team has participated in more than ten EU-funded projects providing agricultural information services that utilize satellite data, drone images, street-level images, geotagged photos from the field and numerical weather predictions to address real-world problems in the thematic areas of i) impacts of climate change on ecosystem services and biodiversity, ii) food security, iii) climate-smart farming, iv) monitoring of land policies.

Through this work, AgriHUB has developed the following novel applications: agrowth (phenology and yield predictions), resagri (weather peril earth warning system), optimal sowing recommendation system (with Corteva) and DataCAP (policy monitoring system). These applications are product of systematic research that has culminated to several scientific publications: crop classification using satellite data and machine learning [1], semi-supervised and unsupervised phenology predictions for real-world scenarios [2], explainable predictions of the onset of pest harmfulness in cotton [3], fusion of satellite and street-level images for enhanced agricultural monitoring [4], agriculture monitoring data cubes and distributed AI pipelines in HPC to handle the massive amounts of satellite data [5], land suitability for applying specific management practices using causal machine learning and assessment of the impact of digital agriculture tools using causal inference [6].

Our team has participated in multiple European projects:

1. RECAP
2. EOPEN
3. ENVISION
4. e-shape
5. Callisto
6. Eiffel
7. Transition
8. Microservices
9. Excelsior

• Monitoring of agricultural policies (e.g., Common Agricultural Policy of the EU)
• Food Security
• Ecosystem Services and Climate Change
• Smart and resilient farming

• Climate Change and Ecosystem Services
  • Understanding the drivers of Earth system change
  • Future trajectories of ecosystem services
• Agriculture Modeling
  • Blending networks and process-based models
  • Blending Earth observations and meteorological data
• Information Extraction from Remote Sensing Images
  • Computer vision on images from heterogeneous sources
  • Big Earth data technologies and distributed learning

• Dr. Dimitrios Barmpoudakis
• Iason Tsardanidis
• Ilias Tsoumas
• Thanassis Drivas
• Dr. Fotios Balampanis

• Dr. Nikolaos S. Bartsotas
• George Choumos
• Nikolaos Kintis

• Dr. Vasileios Sitokostantinou - ([email protected])
• George Giannarakis
• Alkiviadis Koukos

Sitokonstantinou, V., Papoutsis, I., Kontoes, C., Lafarga Arnal, A., Armesto Andres, A. P., and 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, 10(6), 911. Paper 

Rousi, M., Sitokonstantinou, V., Meditskos, G., Papoutsis, I., Gialampoukidis, I., Koukos, A., ... and Kompatsiaris, I. (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. Paper 

Jo, HW., Koukos, A., Sitokonstantinou V., Lee, WK. & Kontoes, C. (2022). Towards Global Crop Maps with Transfer Learning. In NeurIPS Workshop Climate Change AI. Paper 

Choumos, G., Koukos, A., Sitokonstantinou, V. and Kontoes, C. (2022). Towards space-to-ground data availability for the monitoring of the common agricultural policy. In 2022 IEEE 14th Image, Video, and Multidimensional Signal Processing Workshop, IVMSP. Paper 

Ioannidou, M., Koukos, A., Sitokonstantinou, V., Papoutsis, I. and Kontoes, C., 2022. Assessing the added value of Sentinel-1 PolSAR data for crop classification. Remote Sensing. Paper 

Sitokonstantinou, V., Koukos, A., Drivas, T., Kontoes, C., Papoutsis, I., and Karathanassi, V. (2021). A Scalable Machine Learning Pipeline for Paddy Rice Classification Using Multi-Temporal Sentinel Data. Remote Sensing, 13(9), 1769. Paper 

Nanushi, O., Sitokonstantinou, V., Tsoumas, I. and Kontoes, C. (2022). Pest presence prediction using interpretable machine learning. In 2022 IEEE 14th Image, Video, and Multidimensional Signal Processing Workshop, IVMSP. Paper 

Drivas, T., Sitokonstantinou. V., Tsardanidis, I., Koukos, A., Karathanassi, V. and Kontoes, C. (2022). A Data Cube of Big Satellite Image Time-Series for Agriculture Monitoring. In 2022 IEEE 14th Image, Video, and Multidimensional Signal Processing Workshop, IVMSP. Paper 

Sitokonstantinou, V., Koukos, A., Drivas, T., Kontoes, C., and Karathanassi, V. (2022). DataCAP: A Satellite Datacube and Crowdsourced Street-Level Images for the Monitoring of the Common Agricultural Policy. In International Conference on Multimedia Modeling (pp. 473-478). Springer. Paper 

Sitokonstantinou, V., Drivas, T., Koukos, A., Papoutsis, I., Kontoes, C. and Karathanassi, V. (2020). Scalable distributed random forest classification for paddy rice mapping. In Proceedings of the Asian Remote Sensing Conference (ACRS 2019) (pp. 836-845). Paper 

Sitokonstantinou, V., Koukos, A., Kontoes, C., Bartsotas, N. S., and Karathanassi, V. (2021, July). Semi-Supervised Phenology Estimation in Cotton Parcels with Sentinel-2 Time-Series. In 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS (pp. 8491-8494). IEEE. Paper 

Sitokonstantinou, V., Koutroumpas, A., Drivas, T., Koukos, A., Karathanassi, V., Kontoes, H., and Papoutsis, I. (2020). A Sentinel based agriculture monitoring scheme for the control of the CAP and food security. In Eighth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2020) (Vol. 11524, pp. 48-59). SPIE Paper 

Giannarakis, G., Sitokonstantinou, V., Roxanne, L. and Kontoes, C. (2022). Towards assessing agricultural land suitability using causal machine learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Paper 

Giannarakis, G., Sitokonstantinou V., Lorilla, R.S. & Kontoes, C. (2022). Personalizing Sustainable Agriculture with Causal Machine Learning. In NeurIPS Workshop Climate Change AI. Paper 

Tsoumas, I., Giannarakis, G., Sitokonstantinou V., Koukos, A., Loka, D., Bartsotas, N., Kontoes, C. & Athanasiadis, I. (2022). Evaluating Digital Tools for Sustainable Agriculture using Causal Inference. In NeurIPS Workshop Climate Change AI. Paper 

Tsoumas, I., Giannarakis, G., Sitokonstantinou V., Koukos, A., Loka, D., Bartsotas, N., Kontoes, C. & Athanasiadis, I. (2022). Evaluating Digital Agriculture Recommendations with Causal Inference. In Thirty-Seventh AAAI conference on artificial intelligence. paper

• Dr. Roxanne S. Lorilla