Industrial Domains

Space

The Space Industrial domain focuses on creating applications where AI can leverage remote sensing data to develop systems in the space sector that are easily understood by regulatory bodies and adaptable to constantly changing scenarios. There has been growing interest in space development, providing researchers with access to space-based Earth Observation (EO) capabilities to capture critical environmental data, improving accuracy in areas like agriculture and natural disaster management. 

By combining EO with AI, the space industry enables large-scale, near-real-time monitoring of Earth’s surface activities, helping predict and track events such as natural disasters, disease outbreaks, and illegal activities.  

Additionally, it helps identify key correlations between environmental processes and satellite-derived data, offering valuable insights for decision-making and sustainable resource management. 

Sub-Domains
Use Cases
Projects
Project: Quantum-Inspired Tensor Networks for On-Board Hyperspectral Data Processing

Third parties involved: Mazen Ali, Antonio Pereira, Fabio Gentile, Aser Cortines – Multiverse Computing SL 

The project aims to develop a lite AI model capable of running in edge processors, following a multi-model approach, to tackle the challenge of on-board hyperspectral data processing for cloud and cloud shadow masking. Vision Transformers (ViTs), Spectral-Spatial Graph Neural Networks (GNNs), and hybrid CNN-Transformer models will be integrated to enhance feature extraction and classification. Activities include analysing feature selection techniques, model development, validation, and optimization. Expected outcomes are improved accuracy in cloud and cloud shadow detection and suitability for on-board deployment, contributing to ARD hyperspectral data delivery for European hyperspectral satellite missions.   

Project: HYPERBOLA: HYPerspEctRal onBOard cLoud AI

Third parties involved: Maria Banti, Georgios Gousios – Neuralio OÜ 

The project aims to develop a lite AI model capable of running in edge processors, following a multi-model approach, to tackle the challenge of on-board hyperspectral data processing for cloud and cloud shadow masking. Vision Transformers (ViTs), Spectral-Spatial Graph Neural Networks (GNNs), and hybrid CNN-Transformer models will be integrated to enhance feature extraction and classification. Activities include analysing feature selection techniques, model development, validation, and optimization. Expected outcomes are improved accuracy in cloud and cloud shadow detection and suitability for on-board deployment, contributing to ARD hyperspectral data delivery for European hyperspectral satellite missions.