Management: To oversee the project in accordance with the timeline and budget, internal reporting procedures were instituted. Additionally, related activities, such as the self-assessment and data management plans, were coordinated, involving the scientific and technical management to ensure effective execution.
Requirements: Use case scenarios and requirements for all four pilots were specified, refined through dedicated workshops involving end users, and reported in the appropriate deliverables. These were established with careful consideration of the software and hardware technologies integral to CALLISTO.
EO data and other distributed data sources: EO data along with various other distributed data sources are incorporated in the ONDA DIAS platform. This includes EO data derived from Copernicus services, as well as crowdsourced inputs like social media data, footage from UAVs, and direct observations such as hyperspectral reflectance data and air quality measurements.
Machine Learning: Various deep learning models like UNets and ResNets have been trained on heterogeneous, space-to-ground data sources, and used for land-based monitoring including border and agricultural surveillance. Long Short-Term Memory networks have been utilised for air quality forecasting, while Multilayer Perceptrons have been leveraged to transform Sentinel 2 multispectral imagery into hyperspectral data for water quality estimation.
In Data Fusion, the developed 3D visualisation app has enhanced the interpretation of various data sources. Complementing this, an integrated event detection module analyses geo-located social media data pinpointing event locations. MuseHash has been employed to improve the indexing of multimodal data sources, and its performance further optimised. Additionally, the UAV path planning module, also optimised, enhances data acquisition. Lastly, the Multimodal Search Engine has been developed, incorporating both the UAV data indexing and the path planning, establishing it as an all-encompassing tool for UAV missions.
Semantic Technologies: A dedicated ontology for CALLISTO has been created to represent diverse datasets and is publicly available on the VOCOREG platform. The Named Entity recognition service has been enriched with additional languages. A series of SPARQL and GeoSPARQL queries were constructed to evaluate the knowledge graph, and a user-friendly front-end has been implemented for efficient visualization.
CALLISTO Platform: Hosted on ONDA DIAS, has evolved through stages. The initial prototype catered to end-users with data visualization features. This evolved into a second prototype, enhancing tools for content creators. The final prototype focused on new integrated tools dedicated to the administrator users to maintain and operate the platform. Further, a mobile application for Galileo-enabled devices and Mixed Reality User Interface have been developed.
Pilots: Four PUCs have been identified and evaluated. Users’ feedback has been actively collected to ensure prototypes meet end-users' needs and preferences.
Dissemination/Exploitation: CALLISTO's website and social media accounts share updates about the project. Newsletters, pilot brochures, and project videos are readily available on the website. CALLISTO has organized a Joint Hackathon and the Horizon Booster initiative has recognized three of the project's EERs.
Ethics: All ethical and legal related issues such as data protection, privacy, health, safety procedures and general research ethics have been properly and timely addressed, ensuring that the project outcomes abide with the highest standards of research integrity, and especially Horizon Europe Ethics principles.
