Environmental Representation

Semantic environment representation is an important building block for decision-making, planning, and interaction of AI systems in complex and dynamic environments.

AI systems can better understand their environment by structuring relevant information and relationships between objects and events and making them interpretable in context. Different sensor data, contextual information, prior knowledge, and inventory data can be integrated and managed consistently.

This enables AI systems to adapt dynamically to changes in the environment by continuously updating the information relevant to them, taking into account historical data.

Fields of application

The application contexts of semantic environment representation are diverse, so only a few selected examples can be mentioned here.

• Robotic systems use it for environment interpretation, task planning, and navigation, as well as to respond appropriately to events in their environment.
  
• In the field of environmental monitoring, semantic environment representation can be used to analyze environmental data holistically, recognize patterns and trends, and make informed decisions about necessary measures, for example in environmental protection.
• In construction, semantic environment representation enables more precise planning and monitoring of construction projects by integrating relevant information about the site, existing infrastructure, and surrounding elements. In addition, deviations from the original plan can be identified and analyzed; by capturing semantic relationships between materials, equipment, and workers, the efficiency of resource allocation and utilization can also be improved.
• In the field of intelligence and surveillance, semantic environment representation enables in-depth analysis of incoming data and more efficient use of resources (HR and systems). The contextualization and consolidation of information from different sources enables improved interpretation and prioritization of relevant events. Potential risks and threats can be better identified and evaluated in terms of possible consequences and options for action.

Semantic environment representations also promote interoperability between different systems and platforms, facilitating the exchange and sharing of information.

The requirements and characteristics of the underlying models and processes often differ significantly depending on the intended use. Thanks to our extensive experience in the field of sensor-based environment sensing and semantic model generation, we are able to provide tailor-made support for existing and new application domains.