The project will develop a flexible and adaptive system for automated analysis and classification of heterogeneous and unstructured data from different sources using AI methods.

The goal is to develop a knowledge base for decision-making processes within military reconnaissance that can be used for staff work in the context of education, training, and continuing education. This results in the following sub-goals:

• Development of an automated process for the extraction of information from heterogeneous information sources: The goal is to develop technological services for the (semi-)autonomous processing and analysis of multimodal, heterogeneous data that require the use and exploitation of state-of-the-art technologies, i.e., rule-based and AI-based methods for data fusion, analysis, and information extraction.
• Knowledge Modelling: The goal is to create a knowledge base using knowledge modelling methods and technologies for information retrieval from external and open sources of information and knowledge.
• Derive conclusions: The goal is to use the information extracted from the modelled knowledge base to explore the provision of decision support through state-of-the-art technology by automatically testing decision alternatives, deriving new facts and patterns, and predicting future situations and events.

Within the project duration, different technologies will be evaluated based on the selected use cases, merged into a proof-of-concept system and tested. The solutions developed within the R&D activities in BOOST contribute to the following SDGs: SDG#7 Affordable and Clean Energy (Reduction of Ecological Footprint) and SDG #13 Climate Change (Green Defence).