EO4NOWCAST Near Real-Time Soil Moisture Assessment and Pluvial Flood Nowcasting - Backend service
Backend of EO4NOWCAST Near Real-Time Soil Moisture Assessment and Pluvial Flood Nowcasting services
Earth observation plays a crucial role in supporting policy-making processes and developing some economic areas. A vast amount of earth observation data is collected that can feed AI models and methods and thus help to acquire new knowledge and develop innovative products. This section gives references on on-going projects and presents results in the area of AI in earth observation.
Backend of EO4NOWCAST Near Real-Time Soil Moisture Assessment and Pluvial Flood Nowcasting services
This dataset contains corrected PM10 measurements for Sofia Jan-May 2022 fetched from Sensor Community and calibrated with an Extremely Randomised Tree.
OPTIMAL for cOPernicus irrigaTION mAnagement tooLkit is a combination of state-of-the-art Machine Learning techniques that is designed to forecast environmental parameters which enable the delivery of irrigation needs intelligence. The model presented ...
AI E2O.GREEN AI assets pipeline for automated Golf and Grassland Monitoring through interactive maps and computer vision models based on Copernicus data and services.
Climate change, with its accompanying global warming and increasingly severe weather conditions, presents substantial risks to agricultural sustainability worldwide. This is driving the agri-food sector to adapt through the use of new technologies and str...
- Generation of current Soil Moisture (SM) local maps based on the use of a ML model that processes ground-based precipitation measurements and Normalized Difference Moisture Index (NDMI) processed from the last available Copernicus S2 satellite tile. - ...
This asset contains the notebook of the AI model developed in the EO4NOWCAST project, which allows to estimate a current Soil Moisture Map starting from two inputs: - The most recent NDMI map available from Copernicus - The cumulative rainfall map
This AI model developed in the EO4NOWCAST project allows to estimate a current Soil Moisture Map starting from two inputs: - The most recent NDMI map available from Copernicus - The cumulative rainfall map
This dataset contains a set of samples used in the EO4NOWCAST project to train a ML model to predict current soil moisture map in a Area of Interest (in this case, Genoa basin in Italy). Soil Moisture estimation is a crucial parameter for prediction of fl...
Agriculture productivity maps based on satellite images and machine learning algorithms have become powerful tools for understanding and optimizing agricultural practices. By combining the capabilities of satellite imagery and machine learning algorithms,...
A pipeline generation to get and process images from Sentinel-2 for a specific ROI (Region of interest) to display and rescale NVDI values.