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arXiv Machine Learning By Ya Wen, Jixuan Cai, Yulun Zhou, Alec Kirkley

MoRAX: Mobility-based Representation Augmentation for Geospatial Foundation Models

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MoRAX is a lightweight framework that augments geospatial foundation model embeddings with functional structure derived from human mobility data. By incorporating mobility flows, MoRAX preserves the coverage and consistency of existing geospatial models while adding information about functional connectivity among urban regions, enabling zero‑shot deployment in unseen cities. Experiments across four cities in two countries show that the MoRAX teacher model outperforms baseline geospatial models on eight socioeconomic and environmental prediction tasks, and the student model—without direct mobility input—approaches the teacher’s performance.

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