This paper presents the HLIF2024 competition. HLIF2024 is the first competition for High-Level Information Fusion solutions, co-organized with the FUSION2024 conference. The paper presents the challenge use case associated with the competition: information extraction and fusion from Notices to Air Missions data. The challenge is to automate the processing of this data, in order to support pilots with their decision making tasks during flights. To simulate this, the challenge involves a fictitious flight during which information is requested by the pilot. To provide pertinent information to the pilots, the participants are asked to populate a given domain ontology that can then be queried to answer the challenge questions. The participants solutions are evaluated with regard to the correctness, completeness and preciseness of their answers to the questions. The motivation behind the use case is presented, together with the practice dataset that was built and provided to the competition participants. The evaluation methodology and metrics used to rank the participants solutions are detailed. The results of the competition will be presented at the FUSION2024 conference.

The first challenge in crisis response management is the early damage and needs assessment based on all incoming information from various sources such as on field deployed sensors or human observations. Likewise, it is important for an efficient crisis response management to make sure that ambiguous terminology is clearly defined, and methodologies and indicators explained. To support the damage assessment, all the relevant items need to be formally modelled and represented in a machine-readable format. For that purpose, a formal ontology would provide the basis for the verbal description of the current situation understanding as well as the underlying semantics for any fusion related tasks and display operations.

In the context of public place surveillance, the evaluation of available technologies shows that the security bottleneck isn't the surveillance hardware, but rather the real-time analysis and correlation of data provided by various sensors. Also, there is an evident lack of global threat management policy. In this paper, we present an implementation of an event based inference technology called complex event processing, which allows fusion of information generated by heterogeneous sensors supporting the goal of providing a global situational view for public place surveillance. We also present a description logic paradigm to support a non programmer in the situation modeling task. An example of panic crowd detection in an airport illustrates the complete process.

One of the key issues of strategic intelligence within a crisis situation is to build an early assessment of the situation, based on a context sensitive information interpretation and through a well constructed situation representation. Our proposal is based on the conjunction of a conceptual modelling to represent situations out of document analysis and a reactive rule-based modelling to analyze them according to a domain knowledge and a goal. This paper focuses on this situation analysis process. But we present our global approach and sum-up the situation representation and its objectives. We introduce the complex event processing formalism used for the analysis and dynamic recognition of such situations. We illustrate our approach through a case study taken from what happened during the energy crisis in California in 2001