Sat2Flow: A Structure-Aware Diffusion Framework for Human Flow Generation from Satellite Imagery

• URL: http://arxiv.org/abs/2508.19499v1
• Date: Wed, 27 Aug 2025 01:05:37 GMT
• Title: Sat2Flow: A Structure-Aware Diffusion Framework for Human Flow Generation from Satellite Imagery
• Authors: Xiangxu Wang, Tianhong Zhao, Wei Tu, Bowen Zhang, Guanzhou Chen, Jinzhou Cao,
• Abstract summary: Origin-Destination (OD) flow matrices are essential for urban mobility analysis, underpinning applications in traffic forecasting, infrastructure planning, and policy design.<n>We propose Sat2Flow, a latent structure-aware diffusion-based framework that generates structurally coherent OD flows using solely satellite imagery as input.<n> Experimental results on real-world urban datasets demonstrate that Sat2Flow outperforms both physics-based and data-driven baselines in numerical accuracy while preserving empirical distributions and spatial structures under index permutations.
• Score: 7.108993680156414
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Origin-Destination (OD) flow matrices are essential for urban mobility analysis, underpinning applications in traffic forecasting, infrastructure planning, and policy design. However, existing methods suffer from two critical limitations: (1) reliance on auxiliary features (e.g., Points of Interest, socioeconomic statistics) that are costly to collect and have limited spatial coverage; and (2) sensitivity to spatial topology, where minor index reordering of urban regions (e.g., census tract relabeling) disrupts structural coherence in generated flows. To address these challenges, we propose Sat2Flow, a latent structure-aware diffusion-based framework that generates structurally coherent OD flows using solely satellite imagery as input. Our approach introduces a multi-kernel encoder to capture diverse regional interactions and employs a permutation-aware diffusion process that aligns latent representations across different regional orderings. Through a joint contrastive training objective that bridges satellite-derived features with OD patterns, combined with equivariant diffusion training that enforces structural consistency, Sat2Flow ensures topological robustness under arbitrary regional reindexing. Experimental results on real-world urban datasets demonstrate that Sat2Flow outperforms both physics-based and data-driven baselines in numerical accuracy while preserving empirical distributions and spatial structures under index permutations. Sat2Flow offers a globally scalable solution for OD flow generation in data-scarce urban environments, eliminating region-specific auxiliary data dependencies while maintaining structural invariance for robust mobility modeling.

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