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Economy bY spacE

Periodic Reporting for period 1 - EYE (Economy bY spacE)

Reporting period: 2021-07-01 to 2023-06-30

EYE (Economy bY spacE) project addresses the extensive use of remote sensing asset combined with AI techniques and novel economic models to assess a series of economic indicators at different geographical scales for nowcasting and forecasting the impact of natural and man-made global crises and their impact on the European regional economy.
EYE will also provide real time indicators of the spread of the epidemic and its impact on the economy in those countries and geographical areas experiencing Covid-19 outbreak, where direct information and data are not fully reliable and/or provided with large delay. In this respect we also note that current economic models are based on statistical data provided with several months of delay in the best case.
There are a significant number of indirect parameters observable from space (EO parameters) that can be correlated to the impact on the macro and micro economy of natural and man-made disasters as well as with the progression of the diseases. Classical environment parameters (geographical, geomorphological, climatological and hydrogeological) and man induced impact on the environment (urbanisation, pollution, heat) can be combined with economic parameters of human activities impacted by the epidemic including transportation, industry, tourism and trades. These EO parameters may include monitoring of the levels of suspended pollutants, heat distribution around cities and industrial areas, percentage of transport container occupancy in the yard of industrial harbours, car traffic in the cities, parking lots in selected industrial areas, airports, railway stations and the like but also specific one for epidemic like the increase of heat production of crematories or tourism like the number of touristic buses in selected areas, beach occupancy and the like. All these “observed parameters” need to be correlated to macro parameters related to the progress of the epidemic and its impact to the economy at different scales. At medium- and long-term time scale, this methodology enables the near real-time monitoring of macroeconomic parameters during the recovery phase following the end of the emergency outbreak.
The project EYE intends to propose a prototype service, based on the available space asset provided by the European Copernicus services ( integrated with economic and epidemiologic models into an IT platform able to provide automatic image processing supported by artificial intelligence algorithms to assess the impact on the economy of the Covid-19 epidemic at different scales (Metropolitan/ Regional/ National/ European).
The combination of the economic, epidemiology and engineering (remote sensing, image processing, AI) methodologies will contribute to the overall programme and scientific objectives of the project and will constitute the challenge and the research advancement beyond the state-of-the-art.
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, 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 (literature review, impact calculus, etc). 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. A preliminary 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).

IX: Publications: During the project, we have achieved significant progress, resulting in the publication of two scientific papers.

X: Project exploitation and dissemination: In pursuit of effective project exploitation and dissemination of our work, the EYE platform established a website and several social media accounts. Finally, Management and decision-making plan were issued and communicated across the consortium.
The EYE-Sense platform employs a serverless architecture to enhance computational efficiency and reduce cloud expenses. The user-friendly EYE platform functions without requiring coding, allowing researchers to establish connections between earth observation parameters from space (such as night-time lights, atmospheric and water quality, aircraft and ship traffic, etc.) within a specific region and timeframe, and economic datasets provided by the user. By validating our methodology we established a clear connection between Specific Earth observation parameters with activities related to economy. For example we showed the high correlation between night-time lights and touristic activity.

Moreover, 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.

the full implementation of the NLP algorithm which on the EYE platform will provide a powerfull tool to assess the economical impact of a future disaster scenario to a given area. Also, this development will provide a more efficient and accurate tool for analyzing text data, particularly in the context of sentiment analysis of press articles during the pandemic.

The incorporation of STEMA-TIA model with the EYE platform will enable us to directly assess macro-economical indicators (i.e. GDP) of a country using satellite data without having to rely on ground-statistical/economical data that are typically not available. Thus a policy-maker can monitor the Economy of a country and make informed decisions.
EYE platform: Vehicle detection
UCM workshop
UPWR Conference
UPWR Conference (Poland, 05-07/07/2023)
UCM Conference (Spain, 28-30/07/2023)
EYE platform: Night-time lights service
Project Logo
CSEO Conference (Cyprus, 26-27/01/2023)
UCM workshop