Industrial operators face complex evolving demands and significant transformations in decision-making processes, including navigating climate change risks, strengthening disaster preparedness, and responding to evolving pressures and threats to supply chain and logistics operations while investing in manufacturing and delivery processes to ensure carbon neutrality and zero pollution. Robust methodologies are needed to bridge current status-quo descriptions and mapping to finer-scale forecasts and non-expert services, enabling “what-if scenarios” and decision scenario testing for economic and industrial processes.
The objective of this activity is the user-centric development and verification of AI-based capabilities combining EO data with predictive, prescriptive, and contingency planning features, demonstrating their utility in 2-3 use cases.
The focus will be on technical innovation using state-of-the-art AI, such as Reinforcement Learning, Emulators, Graph Neural Networks, Transformers, Federated Learning, and Natural Language Processing. The development will involve consolidating decision scenarios, a user-informed evaluation strategy, priority requirements, and co-developed use cases. It may extend existing services or create new decision support systems for non-EO or geospatial expert users. It is expected to place three contracts addressing different priority issues and analysis methods under this tender.
Learn more about this Invitation To Tender on the esa-star Publication page.