Abstract
Manual verification of potential map inaccuracies can be resource and time intensive. Some systems may utilize a multimodal large language model (LLM) as a reasoning engine for map data validation. An LLM can be configured to ingest and analyze disparate data sources, such as user-generated reports, street-level and satellite imagery, and anonymized aggregate location data. By performing a cross-validation analysis on these inputs, the system may calculate a confidence score for a potential map discrepancy. In some examples, when the score exceeds a predetermined threshold, the system can automatically execute a correction in a geospatial database. This automated correction process may be used to facilitate more efficient and scalable maintenance of map data.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Labzovsky, Ilia and Karmon, Danny, "Multimodal LLM for automated validation and correction of geospatial data.", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/8871