Earth Observation

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.

@ EU Copernicus - https://www.copernicus.eu

Computer vision Earth Observation Company

AMPHITRITE

AI ethics AI services Knowledge Representation Machine learning Multi-agent systems Optimisation Planning and scheduling Robotics and automation Agriculture Cloud, Edge and Infrastructure Earth Observation Energy Healthcare Company

Xibli Sistemas de Informacion SL

AI services Machine learning Robotics and automation Agriculture Earth Observation Company

Digitanimal

AI ethics AI services Automated reasoning Computer vision Connected and automated vehicles Machine learning Optimisation Planning and scheduling Robotics and automation Searching Cloud, Edge and Infrastructure Earth Observation Maritime Sector Public Services Telecommunications Company

North Sea Aviation Services

Earth Observation

SR4C3: Monitoring of the environmental damages in the conflict and fragile areas

Through the integration of the Earth Observation data in the usage of AI based applications we could give an insight into what is happening with the environment in fragile and hard to reach areas. The areas of interest here are Ukraine and Mali.

Dataset

Satellite agriculture productivity index - SAPI

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,...

ML Model

QuantEO

a service for automated clustering of Sentinel-2 pixels which allows its users to focus on Earth surface changes rather than on remote sensing problems, and hence to develop their own downstream applications.

AI ethics AI services Machine learning Searching Earth Observation Public Services Regional Engagement - DIHs Company

Pixstart

Automated reasoning Computer vision Connected and automated vehicles Machine learning Optimisation Planning and scheduling Earth Observation Energy Public Services Regional Engagement - DIHs Transportation Company

Local AI

Executable

Automated pipeline for real time map generation and scalable NVDI values

A pipeline generation to get and process images from Sentinel-2 for a specific ROI (Region of interest) to display and rescale NVDI values.

Dataset

Dataset of phenological stages in grapevines

5 year dataset with information on when phenological states have been reached in 11 different plots in La Rioja - Spain.