Large-scale Review is a research direction on AI-assisted knowledge synthesis. Literature reviews traditionally depend on manual reading of a limited set of papers; we use machine learning, bibliometrics and text mining to extend this to systematic, reproducible synthesis across very large corpora.
The goal is to map whole research fields at scale — how a discipline evolves, where it is going, and how it has responded to major challenges — with methods that are transparent and repeatable.
Reviews
AI in geographical research. A large-scale systematic review of the evolution and current landscape of AI in geography, charting how the field has grown and where it is headed. — Geography and Sustainability, 2026
Advancing intelligent geography. Current status, innovations and future prospects of intelligent geography as a research agenda. — Geography and Sustainability, 2025
COVID-19 geographical research. A systematic review combining machine learning and bibliometric analysis to map geographical research on COVID-19. — Annals of the American Association of Geographers, 2023
Urban land-use mapping. A review of integrating remote sensing and geospatial big data for urban land-use mapping. — International Journal of Applied Earth Observation and Geoinformation, 2021
