LLM-Driven Training-free Location-Attribute Synergic Fusion: A Closed-Loop Paradigm for Dual-source Encrypted POIs and LULC Mapping
Leveraging LLMs to resolve spatial data misalignment in complex land-use mapping workflows
Dual-source encrypted points of interest (DSEP), POIs from two encrypted coordinate systems, suffer from intertwined location and attribute uncertainties, including nonlinear systematic misalignment and naming inconsistency, hindering land-use/land-cover (LULC) mapping. To the be...
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This research introduces a training-free framework that leverages large language models to resolve data misalignment between encrypted, heterogeneous geographic datasets. By automating the fusion of complex POI information, it significantly improves the accuracy of land-use mapping without requiring computationally expensive model retraining.
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