H3-MOSAIC: multimodal generative AI for semantic place detection from high-frequency GPS on H3 grids in mental health geomatics
H3-MOSAIC provides a scalable, auditable pipeline for semantic place detection from high-frequency GPS. Multimodal fusion markedly improves accuracy and consistency. Proprietary models are most robust on hard classes and open-source models are practical for coarse taxonomies. H3 day level exposures show stress patterns consistent with established mental health pathways, supporting face validity. The framework enables downstream exposure analyses with reduced misclassification and improved interpretability.
Authors
- Rachel S. Franklin (ORCID: https://orcid.org/0000-0002-2614-4665)
- John Blake Torous (ORCID: https://orcid.org/0000-0002-5362-7937)
- Jiaee Cheong
- Tongkuai Cong
- Lingbo Liu (ORCID: https://orcid.org/0000-0002-9876-8506)
- A. Oh
- Jin Soo Byun
Institutions
- Beth Israel Deaconess Medical Center (US)
- Harvard University (US)
- Harvard University Press (US)
- Center for Policy Analysis (US)
Publication Details
- Journal
- International Journal of Health Geographics
- Published
- 2025-11-22
- DOI
- https://doi.org/10.1186/s12942-025-00423-9
- Citations
- 1
- Primary Topic
- Digital Mental Health Interventions
- Type
- article
- Field-Weighted Citation Impact
- 1.54
Funders
- National Science Foundation