Deep-learned generation of accurate typical simulator content via multiple geo-specific data channels
US11544832B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Feb 4, 2020 |
| Grant date | Jan 3, 2023 |
| Priority date | — |
| Expiry date | Apr 25, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG09B9/302
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
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.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.