Abstract: A simulation environment is disclosed. In embodiments, the simulation environment includes deep learning neural networks trained to generate photorealistic geotypical image content while preserving spatial coherence. The deep learning networks are trained to correlate geo-specific datasets with input images of increasing detail and resolution to iteratively generate output images until the desired level of detail is reached. The correlation of image input with geo-specific data (e.g., labeled data elements, land use data, biome data, elevational data, near-IR imagery) preserves spatial coherence of objects and image elements between output images, e.g., between adjacent levels of detail and/or between adjacent image tiles sharing a common level of detail.

Abstract: A system and method for deploying software is disclosed. The system includes an architecture for deploying simulation software as a service. The architecture includes a client layer. The client layer includes an edge device, a resource manager, an update framework, a firewall, and a key management system. The architecture further includes a control layer communicatively coupled to the client layer, wherein a portion of the control layer is configured within a server. The control layer includes an application programming interface, one or more containers, wherein at least one of the one or more containers is a simulation processing container. The control layer further includes an orchestration node, a continuous integration tool, one or more processors, and a content delivery network module. The architecture further includes a data layer communicatively coupled to the one or more containers.

Abstract: A simulator environment is disclosed. In embodiments, the simulator environment includes graphics generation (GG) processors in communication with one or more display devices. Deep learning neural networks running on the GG processors are configured for run-time generation of photorealistic, geotypical content for display. The DL networks are trained on, and use as input, a combination of image-based input (e.g., imagery relevant to a particular geographical area) and a selection of geo-specific data sources that illustrate specific characteristics of the geographical area. Output images generated by the DL networks include additional data channels corresponding to these geo-specific data characteristics, so the generated images include geotypical representations of land use, elevation, vegetation, and other such characteristics.

Abstract: A rendering system includes a cloud component and a local edge component. The cloud component receives or retrieves legacy data from various sources and preprocesses the data into a format. The local edge component receives the preprocessed data from the cloud component and performs local rendering steps necessary to place the preprocessed data into a form suitable for a game engine. The game engine utilizes the preprocessed data to render an image stream. The system is embodied in a flight simulator.

Abstract: A system and method for deploying software is disclosed. The system includes an architecture for deploying simulation software as a service. The architecture includes a client layer. The client layer includes an edge device, a resource manager, an update framework, a firewall, and a key management system. The architecture further includes a control layer communicatively coupled to the client layer, wherein a portion of the control layer is configured within a server. The control layer includes an application programming interface, one or more containers, wherein at least one of the one or more containers is a simulation processing container. The control layer further includes an orchestration node, a continuous integration tool, one or more processors, and a content delivery network module. The architecture further includes a data layer communicatively coupled to the one or more containers.

Abstract: A system may include a display and a processor communicatively coupled to the display. The processor may be implemented as a neural network. The processor may be configured to: receive image data associated with a location; receive geo-specific data associated with the location; use the image data and the geo-specific data to generate a photo-realistic image, wherein the photo-realistic image contains photo-typical content in geographically correct locations, wherein the photo-typical content appears typical for the location based at least on the geo-specific data; and output the photo-realistic image to the display for presentation to a user.

Abstract: A simulator environment is disclosed. In embodiments, the simulator environment includes graphics generation (GG) processors in communication with one or more display devices. Deep learning neural networks running on the GG processors are configured for run-time generation of photorealistic, geotypical content for display. The DL networks are trained on, and use as input, a combination of image-based input (e.g., imagery relevant to a particular geographical area) and a selection of geo-specific data sources that illustrate specific characteristics of the geographical area. Output images generated by the DL networks include additional data channels corresponding to these geo-specific data characteristics, so the generated images include geotypical representations of land use, elevation, vegetation, and other such characteristics.

Abstract: A system may include a display and a processor communicatively coupled to the display. The processor may be implemented as a neural network. The processor may be configured to: receive image data associated with a location; receive geo-specific data associated with the location; use the image data and the geo-specific data to generate a photo-realistic image, wherein the photo-realistic image contains photo-typical content in geographically correct locations, wherein the photo-typical content appears typical for the location based at least on the geo-specific data; and output the photo-realistic image to the display for presentation to a user.