We maintain an advanced research testbed to experiment with all aspects of Dense Urban Environment representations including complex geospatial data, underground environments, pattern of life (pedestrian and traffic), cyber civilian infrastructure and more.

Our solutions consider aspects of the entire pipeline for underground representations, spanning from source data collection through to behaviors. Developing simulation engine-agnostic processes and algorithms to procedurally generate game-quality subway networks.

Using cutting-edge voxel technology for the representation of volumetric environments. Developing advanced Artificial Intelligence behaviors to navigate subterranean networks, including mounting subway trains.

Current virtual and constructive simulation often neglect the civilian infrastructure and secondary effects.  Our solutions represent suppliers and consumers representing items such as power, water supply, traffic signals, etc.  This enables modeling of effects of supply disruption.

Provides a set of SNE services to support modeling, simulation, and training use cases.

Correlated and composable services discoverable and executable by SNE system of systems.

Dignitas is using advanced Machine Learning techniques to enable automated discovery of complex geospatial features to derive data for areas where overhead and drive through sensors are ineffective, such as complex underground features.  For example, we are using street level images (such as Google StreetMaps) to automatically detect manhole covers which can then be used to derive sewer line locations.