The Gap Is in the Geometry: A Convergent Meta-Analysis of Multi-Facet Aerial and Remote Sensing Video Understanding

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Computer Science > Computer Vision and Pattern Recognition arXiv:2610.19102 (cs) [Submitted on 13 Oct 2026] Title: The Gap Is in the Geometry: A Convergent Meta-Analysis of Multi-Facet Aerial and Remote Sensing Video Understanding Authors: Georgios Angelidis, Aggelos Psiris, Maria Vakalopoulou, Georgios Th. Papadopoulos, Giorgos Sfikas, Iraklis Varlamis.

计算机科学 > 计算机视觉与模式识别 arXiv:2610.19102 (cs) [提交于 2026 年 10 月 13 日] 标题：差距在于几何：多面航空与遥感视频理解的收敛元分析作者：Georgios Angelidis, Aggelos Psiris, Maria Vakalopoulou, Georgios Th. Papadopoulos, Giorgos Sfikas, Iraklis Varlamis。

Abstract: Aerial and remote sensing video is increasingly evaluated like web video, by posing multiple-choice questions to multimodal large language models (MLLMs) over sampled frames and reporting accuracy. This paper argues that this recipe measures the wrong aspect, and supports the claim with a convergent meta-analysis of a broad set of published results, organized in four evidence tiers, in which a claim becomes a position only when collected evidence agrees.

摘要：航空和遥感视频正越来越多地像网络视频一样被评估，即通过向多模态大语言模型（MLLMs）提出关于采样帧的多项选择题并报告准确率。本文认为这种方法衡量的是错误的方向，并通过对大量已发表结果的收敛元分析支持这一主张。该分析分为四个证据层级，只有当收集到的证据一致时，主张才会成为立场。

Parameter scale exhibits a median of +3.4 accuracy points per tenfold increase on aerial benchmarks, far below a human gap of at least 19 points, whereas acquisition conditions control perception, as 315 of 318 evaluations in 19 within-paper contrasts lose accuracy when conditions such as altitude, view, object size or time horizon become harder.

在航空基准测试中，参数规模每增加十倍，准确率中位数仅提升 3.4 个百分点，远低于至少 19 个百分点的人类差距。与此同时，获取条件控制着感知能力，在 19 篇论文内部对比的 318 项评估中，有 315 项在海拔、视角、物体大小或时间跨度等条件变得更困难时，准确率出现下降。

No aerial benchmark in the corpus combines video input with per-task human and random baselines, competence collapses from answering to acting in closed-loop navigation, and the same MLLMs score higher on the aerial benchmark than on ground spatial ones, but lower on studies that test cross-view geometry. From these findings the current study derives five falsifiable principles and proposes the AVU-Card, a reporting card for aerial video benchmarks.

语料库中没有任何航空基准测试将视频输入与针对任务的人类基准和随机基准相结合；在闭环导航中，模型的能力从回答问题退化到执行动作；同一 MLLMs 在航空基准测试上的得分高于地面空间基准测试，但在测试跨视角几何的研究中得分较低。基于这些发现，本研究推导出了五项可证伪原则，并提出了 AVU-Card，这是一种用于航空视频基准测试的报告卡。