The project aims to develop, train, and apply a hybrid neural network model to optimise EO data for a coherent, balanced water cycle at the global scale resulting in a new pixel-resolution datasets for the four water cycle components: precipitation, evapotranspiration, change in water storage, and runoff (or river discharge). These data will cover the …
The artIficial iNtelligence for Food sEcuRity – INFER – Project aims to foster adoption of AI models for Earth Observation (EO) applications. Many initiatives have been recently launched by ESA to augment the availability of training data, to support new ideas through creation and management of challenges, and to incrementally adopt standards that facilitate interchange …
SD4EO is a study project that aims to demonstrate the benefit of using physically-based simulation data and Artificial Intelligence-based data generation tools in key thematic applications with Earth Observation data and Artificial Intelligence (AI) analytics. The project started in October 2023 and is being led by GMV NSL Ltd (UK), in partnership with the Spanish-based …
SuperIce aims to develop a simulator of high-resolution sea ice thickness in the Arctic to address critical questions related to sea ice predictability at seasonal timescales and its role in the Earth’s climate system. Current satellite-based observations of sea ice thickness provide valuable data but are limited by their spatial resolution. High-resolution information is crucial …
The Deep Earth System Data Lab (DeepESDL) delivers a service to the Earth Science community. The lab provides convenient access to relevant data sets, many of them generated in ESA’s science projects, in an analysis-ready format. Moreover, DeepESDL comprises a powerful data science environment with a focus on machine-learning and artificial intelligence workflows. We are …
Development and testing of novel Deep Learning based methods for detection and classification of priority features of interest (tents, vehicles, rotary wing aircraft etc).
The AIOPEN project will combine and extend the existing platform ASB (Automated Service Builder), EOPEN (Open Interoperable Platform for Unified Access & Analysis of EO Data) and EOEPCA (EO Exploitation Platform Common Architecture) with new and innovative services based on operationally mature AI/ML software capabilities to provide a platform that supports the end-to-end AI model …
The primary objective of this project is to augment the capabilities of Ellip-powered Exploitation platforms, specifically Geohazards Exploitation Platform (GEP) and Urban Thematic Exploitation Platform (U-TEP), by seamlessly integrating an AI/ML processing framework. This comprehensive framework will encompass the entire machine learning pipeline, including data discovery, access to training data, model development and deployment. GEP …
Development of AI based methods to classify herds of different animals based on the spatial distribution patterns adopted within different herd species. AI based pattern recognition methods based on distribution patterns of animals within a herd detected in EO imagery.
Illegal excavation of archaeological sites aimed at collecting historical material culture (‘looting’) to introduce it in the illicit market of antiquities is a pressing problem on a global scale. Under favourable circumstances, looting can be exposed on Earth Observation (EO) data by detecting changes that have occurred between two or more consecutive EO images of …
