卫星能看到通勤者吗？视觉基础模型的关键基准
Do Satellites See Commuters? A Critical Benchmark of Vision Foundation Models
- The University of New South Wales（新南威尔士大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究在WeDAN框架下对比4种卫星视觉编码器的通勤OD生成性能，发现地理接地型编码器零样本迁移更优，跨大洲OD生成仍是未解决问题。
AI中文摘要：
卫星基础模型为通勤起讫点（OD）生成提供了人口普查数据之外的全球可用替代方案，但尚无研究在同一下游流程中系统对比编码器范式。我们在相同的WeDAN图扩散框架内，对4种卫星视觉编码器进行了消融实验：语言监督型（RemoteCLIP）、自监督型（DINOv3）以及地理接地型（SatCLIP、AlphaEarth），实验覆盖美国1925个县、英国325个区和全球14个城市，采用5组随机种子。得出3项主要发现：其一，语言监督特征在分布内表现最强（RemoteCLIP的CPC为0.602），而地理接地型编码器的零样本迁移更可靠：AlphaEarth在英国区的CPC较RemoteCLIP提升33%；其二，仅预训练语料库规模不足：在规模大得多的卫星语料上训练的DINOv3，分布内CPC较RemoteCLIP低0.091，全局CPC仅0.022；其三，无编码器可有效迁移至全球城市（RemoteCLIP最佳CPC为0.122，DINOv3为0.022），证实跨大洲OD生成仍是未解决的问题。我们还阐明了人口普查噪声参数η的语义，其在跨大洲评估下的排序会反转，这一区别对正确解读先前结果至关重要。训练脚本和评估日志将被公开。
英文摘要：
Satellite foundation models offer a globally available alternative to census data for commuting origin-destination (OD) generation, yet no study has systematically compared encoder paradigms within a single downstream pipeline. We ablate four satellite vision encoders: language-supervised (RemoteCLIP), self-supervised (DINOv3), and geographically grounded (SatCLIP, AlphaEarth) within an identical WeDAN graph diffusion framework across 1,925 US counties, 325 UK districts, and 14 global cities under five random seeds. Three main findings emerge. First, language-supervised features achieve the strongest in-distribution performance (RemoteCLIP CPC 0.602), while geographically grounded encoders transfer more reliably zero-shot: AlphaEarth improves CPC by 33% over RemoteCLIP on UK districts. Second, pretraining corpus scale alone is insufficient: DINOv3, trained on a substantially larger satellite corpus, underperforms RemoteCLIP by 0.091 CPC in-distribution and collapses to CPC 0.022 globally. Third, no encoder transfers usefully to global cities (best CPC 0.122 for RemoteCLIP, 0.022 for DINOv3), confirming cross-continental OD generation remains an open problem. We additionally clarify the semantics of the census noise parameter $η$, whose ordering reverses under cross-continental evaluation, a distinction critical to correctly interpreting prior results. Training scripts and evaluation logs will be released.
