EYE-Sense is a web-based support system for integrated socio-economic and epidemics analysis based on geo-informatics, AI and information technology modeling. EYE-Sense was developed with the intention of assisting interdisciplinary research and allowing an individual to access information that would traditionally require a strong technical background to obtain. ​

The integration of geo-informatics, computer vision, and information technology modeling has created a growing demand for sophisticated support systems such as EYE-Sense. As various sectors—including urban planning, environmental management, and economic development—seek to leverage data for better decision-making, tools that facilitate advanced socio-economic analysis are increasingly valued.

Modular Components

• Serverless Architecture: To provide cost-efficiency, scalability, flexibility, and easy maintenance, EYE-sense adopts a serverless architecture, leading to up to 50.4% processing cost reduction when compared to traditional server-based solutions. By bridging the gap between data gathering and processing, EYE- sense extends the reach of Earth observation data to a broader audience.

  

• Media Sentiment Analysis: The Media Sentiment Analysis is a component that offers the ability to determine the emotional tone (positive, negative, or neutral) of textual data derived from newspaper articles. It allows for the identification of correlations between media sentiment and traditional economic indicators such as the Purchasing Managers’ Index (PMI) and average employment in the enterprise sector. The component leverages various statistical methods, including correlation analysis with temporal shifts , and AI models, specifically pre-trained Natural Language Processing (NLP) models designed for sentiment analysis in Polish.

  

• Epidemic Models: UTH’s participation in the EYE Project has produced models and tools to advance the knowledge on the relationship between climate, environment and COVID-19 spread and severity, by identifying associations with specific air pollutant and climate variables.

  

• STEMA-TIA: The STeMA-TIA model has been devised to support an integrated vision of COVID impact, as territorialised and sectoral at NUTS 2 and 3 levels in some areas (metro and islands: Poland, Italy, Greece, Cyprus). This assessment tool was adapted to the project scope. The method, the ex ante application and some examples have been discussed during the EYE project formal meetings. By giving evidence to the COVID19 territorial/economic impacts within EU framework at the regional and sub-regional level, the elaborated STeMA-TIA adaptation was able to: i) know and select effects on territorial status quo indicators before; ii) measure the degree of risk of overtaking the carrying capacity threshold and the improvement in performance and sustainability; iii) build scenarios on the base of time series.

  

• Territorial analysis and data: Through the collection, analysis, and representation of socioeconomic data—carried out also during secondment activities—it was possible to strengthen the territorial information system related to the Sardinia region, enriching it with updated indicators useful for guiding future policy decisions. The knowledge base covers the period immediately following the onset of the pandemic up to the present day, allowing for evaluations and comparisons—particularly in relation to the tourism sector—that can highlight differences, growth trajectories, and development prospects, taking into account the environmental, economic, and social dimensions of the phenomenon.

  

  

• EYE time series Analyzer: The Time Series Analyzer is a part of the EYE-Sense platform that offers users the ability to perform time series analysis, identify correlations, and make forecasts by utilizing statistical methods and AI models.

  

• Computer Vision models: Computer vision models designed to identify ships, aircraft, cars, umbrellas, and containers in ultra high-resolution satellite images.       

  

• AI Wizard (AI Assistant): An AI assistant that helps guide users through the results of the EYE-Sense platform and provides explanations for better understanding.