aqua3S

Enhancing Standardisation strategies to integrate innovative technologies for Safety and Security in existing water networks

Exposure of citizens to potential disasters has led to vulnerable societies that require risk reduction measures. Drinking water is one of the main risk sources when its safety and security are not ensured. aqua3S combines novel technologies in water safety and security, aiming to standardize existing sensor technologies complemented by state-of-the-art detection mechanisms. Hence, it could propose innovative solutions to water facilities and responsible authorities in order to detect and tackle water-related crises in a timely manner.

On the one hand, sensor networks are deployed in water supply networks and sources, supported by complex sensors for enhanced detection; on the other hand, sensor measurements are supported by videos from Unmanned Aerial Vehicles (UAVs), satellite images and social media observations from citizens that report low-quality water in their area (e.g. by colorization); introducing this way a bottom-up approach which raises social awareness and, also, promotes interactive knowledge sharing. Semantic representation and data fusion provide intelligent DSS alerts and messages to the public through first responders’ mediums.

The proposed technical solution is designed to offer a very effective detection system, taking into account the cost of the aqua3S platform and target at a very high return-on-investment ratio. The main strategy for the integration of aqua3S’ solution into the market is designed on the standardization of the proposed technologies and the project’s secure platform.

PUBLICATIONS

  1. D.F. Mantsis, A. Moumtzidou, I. Lioumbas, I. Gialampoukidis, A. Christodoulou, A. Mentes, S. Vrochidis , and I. Kompatsiaris, "Detection of Complex Formations in an Inland Lake from Sentinel-2 Images Using Atmospheric Corrections and a Fully Connected Deep Neural Network," Remote Sensing, vol. 16(20), pp. 3913, 2024. https://doi.org/10.3390/rs16203913
  2. K. Vlachos, A. Moumtzidou, I. Gialampoukidis, S. Vrochidis, I. Kompatsiaris, "A tempοral Deep Convolutional Neural Network model on Sentinel-1 Image Time Series for pixel-wise Flood Classification". In Proceedings of the 2022 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2022), Kuala Lumpur, Malaysia, 17-22 July 2022, Physical, IEEE. OPEN ACCESS
  3. S. Andreadis, N. Pantelidis, I. Gialampoukidis, S. Vrochidis, and I. Kompatsiaris, "Water quality issues: Can we detect a creeping crisis with social media data?", In the proceedings of the 27th IEEE Symposium on Computers and Communications (IEEE ISCC 2022), 30 June - 3 July 2022, Rhodes, Greece.
  4. D. F. Mantsis, M. Bakratsas, K. Vlachos, A. Moumtzidou, I. Gialampoukidis, S. Vrochidis, I. Kompatsiaris "Detection of oil spills in an inland lake using Multi-Spectral Satellite Images". In Proceedings of the 2022 Living Planet Symposium, 23-27 May 2022, Bonn, Germany. OPEN ACCESS
  5. M. Ferri, F. Lombardo, D. Norbiato, A. Moumtzidou, A. Karakostas, S. Vrochidis, and I. Kompatsiaris "Exploring the potential of innovative methods for integrated water and flood risk management in the Oriental Alpes river district: the aqua3S approach".In WMS-EXPO2020: Water Resources Management and Sustainability: Solutions for Arid Regions, Dubai Exhibition Center (DEC), Dubai, UAE, March 22-24, 2022.
  6. G. Antzoulatos, I. Kouloglou, M. Bakratsas, A. Moumtzidou, I. Gialampoukidis, A. Karakostas, F. Lombardo, R. Fiorin, D. Norbiato, M. Ferri, A. Symeonidis, S. Vrochidis, I. Kompatsiaris, "Flood Hazard and Risk Mapping by Applying an Explainable Machine Learning Framework Using Satellite Imagery and GIS Data". Special Issue: Environmental Water Monitoring for Sustainable Development in Urban and Rural Areas of Sustainability, Volume 14, Issue 6, 2022. DOI:10.3390/su14063251. OPEN ACCESS
  7. S. Andreadis, I. Gialampoukidis, A. Bozas, A. Moumtzidou, R. Fiorin, F. Lombardo, A. Karakostas, D. Norbiato, S. Vrochidis, M. Ferri, and I. Kompatsiaris, "WaterMM: Water Quality in Social Multimedia Task at MediaEval 2021". In Proceedings of the MediaEval 2021 Workshop (MediaEval'21), 12-15 December 2021.
  8. Α.-Μ., Koufakis, S., Tzanakis, A., Moumtzidou, G., Meditskos, A., Karakostas, S., Vrochidis, I., Kompatsiaris, "OntoAqua: Ontology-based Modelling of Context in Water Safety and Security", In proceedings of the 13th International Conference on Knowledge Engineering and Ontology Development-KEOD 2021, Valletta, Malta, 25-27 October 2021.
  9. I. Gialampoukidis , A. Moumtzidou, M. Bakratsas , S. Vrochidis, I. Kompatsiaris, "A multimodal tensor-based late fusion approach for satellite image search in Sentinel 2 images", In Proceedings of the 27th International Conference on Multimedia Modeling, (MMM 2021), Prague, Czech Republic, 22-24 June, 2021. DOI:10.1007/978-3-030-67832-6. OPEN ACCESS
  10. S. Andreadis, G. Antzoulatos, T. Mavropoulos, P. Giannakeris, G. Tzionis, N. Pantelidis, K. Ioannidis, A. Karakostas, I. Gialampoukidis, S. Vrochidis, I. Kompatsiaris "A social media analytics platform visualising the spread of COVID-19 in Italy via exploitation of automatically geotagged tweets". Online Social Networks and Media, 2021. OPEN ACCESS
  11. K. Gkountakos, D. Galanopoulos, M. Mpakratsas, D. Touska, A. Moumtzidou, K. Ioannidis, I. Gialampoukidis, S. Vrochidis, V. Mezaris, I. Kompatsiaris, "ITI-CERTH participation in TRECVID 2020", Proc. TRECVID 2020 Workshop, Gaithersburg, MD, USA, Dec. 2020. OPEN ACCESS
  12. M. Bakratsas; A. Moumtzidou; I. Gialampoukidis; S. Vrochidis; I. Kompatsiaris: "Sentinel 1 and DEM Data Fusion with Neural Network Classification in Water Body Detection", EO FOR WATER CYCLE SCIENCE (EO4WATER 2020), Online Event, 16 -19 November 2020. OPEN ACCESS
  13. A. Moumtzidou, M. Bakratsas, S. Andreadis, A. Karakostas, I. Gialampoukidis, S. Vrochidis, I. Kompatsiaris, "Flood detection with Sentinel-2 satellite images in crisis management systems", in 17th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2020), Blacksburg, Virginia, USA, 24-27 May 2020. OPEN ACCESS
  14. G. Antzoulatos, I. Koulalis, A. Karakostas, S. Vrochidis, I. Kompatsiaris, “Towards to integrate a multi-layer Machine Learning Data Fusion approach into Crisis Classification and Risk Assessment of extreme natural events”. In "Technology Development for Security Practitioners", edited by B. Akhgar, D. Kavallieros, E. Sdongos, Book series: Security Informatics and Law Enforcement, Springer, 2021. OPEN ACCESS