Deep-Learned Geo-Specific Image Generator with Spatial Coherence
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Solution Overview
Problem
High-resolution image generation methods, such as deep learning neural networks, fail to produce spatially coherent imagery, leading to inconsistent representation of terrain features across multiple images, including position, orientation, and characteristics like color and texture.
Innovation Solution
A simulator environment with graphics generation processors and non-transitory memory that stores spatially coherent basemap images and geo-specific datasets, where deep learning neural networks are trained to generate images by correlating input images with geo-specific data to produce spatially coherent output images of increasing detail until a desired level is reached.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning neural networks are used to generate high-resolution imagery, then photorealistic quality is achieved, but spatial coherence is lost
Solution Approach 1:
The patent introduces an intermediary system comprising a coordinate transformation module and a feature integration module. The coordinate transformation module transforms features from different image coordinates to a common reference frame, while the feature integration module combines these transformed features. This intermediary processing ensures that features from multiple input images are spatially aligned and coherent in the generated high-resolution output, resolving the contradiction between photorealistic quality and spatial coherence.
2Area of stationary object
If multiple generated images are created to represent terrain, then coverage area increases, but consistency of features across images deteriorates
Solution Approach 1:
The patent segments the terrain representation task into multiple specialized modules: a coordinate transformation module for spatial alignment, a feature integration module for combining features, and a high-resolution generation module for detail enhancement. Each module processes specific aspects of the terrain data independently, allowing the system to generate multiple images with consistent features across the entire coverage area while maintaining photorealistic quality in each segment.
Data Source
AI summary
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.