arXiv AI By Zhiyong Zhou, Song Gao, Qianheng Zhang, Feng Zhang, Zhenhong Du

Quantifying geographic domain shift to decouple the geospatial transferability of human mobility flow generation models

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arXiv Machine Learning
Aug 19

MoRAX: Mobility-based Representation Augmentation for Geospatial Foundation Models

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.

By Ya Wen, Jixuan Cai, Yulun Zhou, Alec Kirkley
arXiv AI
Aug 19

MoRA: Mobility as the Backbone for Geospatial Representation Learning at Scale

MoRA is a human‑centric geospatial representation learning framework that uses a large mobility graph as its backbone to fuse spatial tokenization, graph neural networks, and asymmetric contrastive learning. It aligns over 100 million points of interest, massive remote sensing imagery, and structured demographic data with a billion‑edge mobility graph, producing compact 128‑dimensional embeddings that capture socio‑economic context and functional roles of locations. On a benchmark of nine downstream social and economic prediction tasks, MoRA outperforms state‑of‑the‑art models by an average of 12.9% and demonstrates scaling behavior analogous to large language models.

By Ya Wen, Jixuan Cai, Qiyao Ma, Linyan Li, Xinhua Chen, Chris Webster, Yulun Zhou