Deep-learned photorealistic geo-specific image generator with enhanced spatial coherence
US11694089B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Feb 4, 2020 |
| Grant date | Jul 4, 2023 |
| Priority date | — |
| Expiry date | Nov 22, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/045
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
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.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.