2026-09-02 –, Ran1
We use LLM to extract and structure geospatial data buried in 100K–1M+ PDF and Office files held by Japan's MLIT, enabling visualization, spatial analysis, and evidence-based policymaking — demonstrated through real-world use cases, no coding required.
By combining LLM-based structuring with spatial joins, we achieve robust data integration that goes beyond simple text matching.
Key features:
- Batch structuring and high-speed parallel processing of PDF, Excel, Word, and PowerPoint files using LLM
- Data cleansing, geocoding, and spatial joins to reconstruct documents as geospatial data
- Spatial analysis and visualization leveraging the rich geographic density unique to MLIT datasets
- End-to-end pipeline from data extraction through anonymization to open data publication
Who Should Attend:
- Government and municipal officials interested in digital transformation and data infrastructure
- Researchers and think tank professionals involved in EBPM
- Data engineers, GIS developers, and no-code/low-code developers
- Startups and corporate representative working on projects that utilize open or public data
I am leading the development of an application to be introduced in the talk.