Periodic Reporting for period 2 - EYE (Economy bY spacE)
Período documentado: 2023-07-01 hasta 2025-06-30
The challenge of understanding real-time economic conditions, rather than relying on outdated reports, is the core issue addressed by EYE (Economy bY spacE). Traditional economic reports often require months to compile, leading decision-makers to rely on lagging indicators. EYE aims to mitigate this limitation by offering a more rapid and responsive method of assessing economic conditions, even in regions where data collection is challenging.
The project leverages satellite imagery and artificial intelligence combined with Economic and Epidemic models to provide insights into current economic activity, particularly during global crises such as natural disasters or disease outbreaks.
This innovative approach enables the nowcasting and forecasting of economic indicators across various geographical scales, particularly within the European regional economy. The reliance on delayed statistical data in current economic models hinders timely and effective responses to rapidly evolving situations. The EYE platform addresses this limitation by providing near-real-time indicators of epidemic spread and economic impacts, especially in areas where direct data is unreliable or incomplete.
EYE's fusion of economic, epidemiological, remote sensing, and AI methodologies advances beyond the state of the art, delivering a user-friendly platform for timely, data-driven situational awareness and policy support.
The project leverages satellite imagery and artificial intelligence combined with Economic and Epidemic models to provide insights into current economic activity, particularly during global crises such as natural disasters or disease outbreaks.
This innovative approach enables the nowcasting and forecasting of economic indicators across various geographical scales, particularly within the European regional economy. The reliance on delayed statistical data in current economic models hinders timely and effective responses to rapidly evolving situations. The EYE platform addresses this limitation by providing near-real-time indicators of epidemic spread and economic impacts, especially in areas where direct data is unreliable or incomplete.
EYE's fusion of economic, epidemiological, remote sensing, and AI methodologies advances beyond the state of the art, delivering a user-friendly platform for timely, data-driven situational awareness and policy support.
I. State of the art review:
The project started with an extensive investigation of the satellite data and sources available, with a focus on identifying suitable time frames aligned with the project's objectives. This led to the initial selection of the most promising Earth Observation (EO) parameters, taking into account both ease-of-use and their potential correlation with economic and epidemic data.
II. Analysis and selection of AI methods: We successfully developed and tested computer vision and image feature extraction algorithms tailored specifically for detecting airplanes, ships, containers, vehicles and umbrellas in satellite images with diverse resolutions.
III. Analysis and definition of economic key indicators and indexes: The general framework about econometric indicators was defined and developed within the declared methodological approach. Territorial/grey/statistical indicators were identified and selected to be correlated against EO parameters.
IV. Analysis and definition of epidemic key indicators and indexes: Epidemic key indicators and models were selected and justified. An epidemic analysis was carried out for the region of Macedonia in Greece on which the selected epidemic models were combined with weather information from satellite data.
V. Econometric model analysis and trade-off: The development and application and deployment of STeMA Territorial Impact Assessment (TIA) methodology and techniques were succesfully implemented.
Logical chain and GIS operational tool was deployed succesfully, including co-research and development of statistical and geographical appropriate indicators, for measuring and assessing various policies, local practices and impacts of epidemy at regional and local level. Two cases studies were carried out for Rome (Italy) and Wronclaw (Poland) in order to test the STeMA-TIA methodology. Moreover a selection of quali-quantitative statistical/geographical indicators, Building of composite indices, cooperation to Data collection at local level (per case) was carried out in order to build Case-studies to validate the developed tools and methodologies .
VI. Epidemic model analysis and implementation: The epidemic model was completed and the software was analysed and tested for later integration in the EYE platform.The model was validated through a use-case in Macedonia (Greece).
The EYE project main result includes the development of the EYE-SENSE platform through which local and public authorities will be able effortlessly gain access to Earth Observation (EO) parameters of a selected area related to atmospheric and water quality, night-light activity, land-surface temperature and object detection. Those EO parameters can be used to directly analyze a region’s socio-economic activity.
Other main results are:
1. Serverless Architecture Implementation: The platform adopts a serverless architecture, leading to potential cost savings and improved scalability.
2. Additional Modules: Development of a media sentiment analysis tool, epidemic models, and a territorial analysis model.
3. Datasets: Generation of valuable datasets, including earth observation data and in-situ economic data.
The exploitable assets of the EYE project can be categorized into four (4) groups as described below:
1. EYE Platform and modules: This includes any custom algorithms, AI/Computer vision models, Natural Language Processing (NLP) models, STEMA-TIA models, Epidemic models, data and processing pipelines, or analytical tools developed as part of the project.
2. Data Products and Datasets: The project generated valuable datasets, including earth observation data, and in-situ economic data. These datasets can be packaged and distributed as data products for research, planning, and decision-making purposes.
The project started with an extensive investigation of the satellite data and sources available, with a focus on identifying suitable time frames aligned with the project's objectives. This led to the initial selection of the most promising Earth Observation (EO) parameters, taking into account both ease-of-use and their potential correlation with economic and epidemic data.
II. Analysis and selection of AI methods: We successfully developed and tested computer vision and image feature extraction algorithms tailored specifically for detecting airplanes, ships, containers, vehicles and umbrellas in satellite images with diverse resolutions.
III. Analysis and definition of economic key indicators and indexes: The general framework about econometric indicators was defined and developed within the declared methodological approach. Territorial/grey/statistical indicators were identified and selected to be correlated against EO parameters.
IV. Analysis and definition of epidemic key indicators and indexes: Epidemic key indicators and models were selected and justified. An epidemic analysis was carried out for the region of Macedonia in Greece on which the selected epidemic models were combined with weather information from satellite data.
V. Econometric model analysis and trade-off: The development and application and deployment of STeMA Territorial Impact Assessment (TIA) methodology and techniques were succesfully implemented.
Logical chain and GIS operational tool was deployed succesfully, including co-research and development of statistical and geographical appropriate indicators, for measuring and assessing various policies, local practices and impacts of epidemy at regional and local level. Two cases studies were carried out for Rome (Italy) and Wronclaw (Poland) in order to test the STeMA-TIA methodology. Moreover a selection of quali-quantitative statistical/geographical indicators, Building of composite indices, cooperation to Data collection at local level (per case) was carried out in order to build Case-studies to validate the developed tools and methodologies .
VI. Epidemic model analysis and implementation: The epidemic model was completed and the software was analysed and tested for later integration in the EYE platform.The model was validated through a use-case in Macedonia (Greece).
The EYE project main result includes the development of the EYE-SENSE platform through which local and public authorities will be able effortlessly gain access to Earth Observation (EO) parameters of a selected area related to atmospheric and water quality, night-light activity, land-surface temperature and object detection. Those EO parameters can be used to directly analyze a region’s socio-economic activity.
Other main results are:
1. Serverless Architecture Implementation: The platform adopts a serverless architecture, leading to potential cost savings and improved scalability.
2. Additional Modules: Development of a media sentiment analysis tool, epidemic models, and a territorial analysis model.
3. Datasets: Generation of valuable datasets, including earth observation data and in-situ economic data.
The exploitable assets of the EYE project can be categorized into four (4) groups as described below:
1. EYE Platform and modules: This includes any custom algorithms, AI/Computer vision models, Natural Language Processing (NLP) models, STEMA-TIA models, Epidemic models, data and processing pipelines, or analytical tools developed as part of the project.
2. Data Products and Datasets: The project generated valuable datasets, including earth observation data, and in-situ economic data. These datasets can be packaged and distributed as data products for research, planning, and decision-making purposes.
The EYE-SENSE platform employs a serverless architecture for enhanced computational efficiency and reduced cloud expenses. The platform functions without requiring coding, allowing researchers to establish connections between space-based earth observation parameters (night-time lights, atmospheric and water quality, aircraft and ship traffic, etc.) within specific regions and timeframes, with user-provided economic datasets. By validating the methodology, EYE established connections between specific EO parameters and economic activity. For example, EYE showed a high correlation between night-time lights and touristic activity.
Further advancements include:
Incorporation of a Natural Language Processing (NLP) algorithm to assess the economic impact of future disaster scenarios.
Integration of the STEMA-TIA model to assess macro-economic indicators (i.e. GDP) using satellite data without reliance on ground-based data.
The EYE project advances beyond the state of the art by:
Integrating Diverse Data and Technologies: Combines remote sensing data with AI, econometric models, and epidemiological models into an integrated IT platform.
Providing Near Real-Time Monitoring: Delivers near real-time indicators of economic activity and epidemic spread, addressing delays in traditional data collection and modeling.
Leveraging Serverless Architecture: Improves cost efficiency, scalability, and flexibility.
Offering a User-Friendly Interface: Makes complex data and analytics accessible to users without strong technical backgrounds.
Focusing on Touristic Areas: Monitors economic activity in touristic areas.
These advancements enable policymakers to monitor a country's economy and make informed decisions based on real-time insights.
Potential Impacts
By validating the methodology, EYE established clear connections between Specific Earth observation parameters with activities related to economy. The EYE platform empowers policymakers to make well-informed choices and precise predictions pertaining to a particular scenario (i.e. touristic activity), without being solely dependent on typically unavailable economic or statistical information.
Further advancements include:
Incorporation of a Natural Language Processing (NLP) algorithm to assess the economic impact of future disaster scenarios.
Integration of the STEMA-TIA model to assess macro-economic indicators (i.e. GDP) using satellite data without reliance on ground-based data.
The EYE project advances beyond the state of the art by:
Integrating Diverse Data and Technologies: Combines remote sensing data with AI, econometric models, and epidemiological models into an integrated IT platform.
Providing Near Real-Time Monitoring: Delivers near real-time indicators of economic activity and epidemic spread, addressing delays in traditional data collection and modeling.
Leveraging Serverless Architecture: Improves cost efficiency, scalability, and flexibility.
Offering a User-Friendly Interface: Makes complex data and analytics accessible to users without strong technical backgrounds.
Focusing on Touristic Areas: Monitors economic activity in touristic areas.
These advancements enable policymakers to monitor a country's economy and make informed decisions based on real-time insights.
Potential Impacts
By validating the methodology, EYE established clear connections between Specific Earth observation parameters with activities related to economy. The EYE platform empowers policymakers to make well-informed choices and precise predictions pertaining to a particular scenario (i.e. touristic activity), without being solely dependent on typically unavailable economic or statistical information.