Abstract
Current mapping systems present geographic data, such as satellite imagery and 3D map layers, in a static manner, limiting the user's ability to visualize environmental changes over time. This disclosure describes a system and technique that leverages generative artificial intelligence (GenAI) and neural radiance fields (NeRF) to dynamically render geographic data, simulating seasonal and temporal variations. This approach transforms map data based on user prompts or inferred seasonal/temporal data and enables novel viewpoint generation and animation of 3D map views. The system enhances realism, provides a more informative user experience, and has applications in various fields, including navigation, urban planning, and virtual tourism.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Johnson, Joseph Edwin Jr. and Johnson, Quinn Thuy, "Dynamic Rendering of Views of Geographic Data with Generative AI for Seasonal and Temporal Variations", Technical Disclosure Commons, (May 28, 2025)
https://www.tdcommons.org/dpubs_series/8164