Summary
LiveEO will offer a holistic monitoring service for infrastructure networks based on satellite data and machine learning algorithms to identify external threats to the grid and predict future impacts by adding time series analytics to our services. The service will enable rapid change
detection (e.g. storm monitoring) to quickly assess the location and extent of damage and slow change detection to facilitate the prediction of potential risks. We will make use of optical and radar data to offer services in real time, regardless of weather. The solution requires the
development of AI-based change detection algorithms that evaluate data at different points in time i.e. time-series and are able to detect patterns and abnormalities in the data with high precision. To do this automatically, the development of an automated process chain, able to transform
data into actionable insights for our customers is part of the solution. Insights will be made available via mobile and web apps.
detection (e.g. storm monitoring) to quickly assess the location and extent of damage and slow change detection to facilitate the prediction of potential risks. We will make use of optical and radar data to offer services in real time, regardless of weather. The solution requires the
development of AI-based change detection algorithms that evaluate data at different points in time i.e. time-series and are able to detect patterns and abnormalities in the data with high precision. To do this automatically, the development of an automated process chain, able to transform
data into actionable insights for our customers is part of the solution. Insights will be made available via mobile and web apps.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/190100375 |
| Start date: | 01-10-2022 |
| End date: | 31-03-2024 |
| Total budget - Public funding: | 2 489 000,00 Euro - 1 735 300,00 Euro |
Cordis data
Original description
LiveEO will offer a holistic monitoring service for infrastructure networks based on satellite data and machine learning algorithms to identify external threats to the grid and predict future impacts by adding time series analytics to our services. The service will enable rapid changedetection (e.g. storm monitoring) to quickly assess the location and extent of damage and slow change detection to facilitate the prediction of potential risks. We will make use of optical and radar data to offer services in real time, regardless of weather. The solution requires the
development of AI-based change detection algorithms that evaluate data at different points in time i.e. time-series and are able to detect patterns and abnormalities in the data with high precision. To do this automatically, the development of an automated process chain, able to transform
data into actionable insights for our customers is part of the solution. Insights will be made available via mobile and web apps.
Status
SIGNEDCall topic
HORIZON-EIC-2022-ACCELERATOROPEN-01Update Date
09-02-2023
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