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.
The dataset was designed for prediction of vegetation health on Sentinel-2 imagery and utilized in the AI4Copernicus service: “Long Short-Term Memory Neural Network for NDVI prediction" in which an LSTM neural network was trained in order to create an AI ...
The dataset was designed for semantic-segmentation-based deep learning models and utilized in the AI4Copernicus service: “Deep network for pixel-level classification of S2 patches" in which a U-net neural network was used in order to create an AI model fo...
We introduce SemiLake, a semi-supervised representation learning-powered urban lakes and algae monitoring system.
AQQA was an AI4Copernicus micro-project, selected in the 5th round of open calls. It aimed to test a language-based interaction with air quality data by making use of linked geospatial data tools and the health bootstrapping service provided by the AI4Cop...
Land type: build-up, rural, forest, and water
The project SuperPower 2.0 developed thanks to I-NERGY open calls has developed an AI tool for automatic detection of broken insulators using a YOLO architecture. The end-goal of the developed system is to be able to provide end clients (power line own...