Scaling laws assume larger models trained on more data consistently outperform smaller ones -- an assumption that drives model selection in computer vision but remains untested in resource-constrained Earth observation (EO). We conduct a systematic efficiency analysis across three scaling dimensions: model size, dataset size, and input resolution, on rooftop PV detection in Madagascar. Optimizing for model efficiency (mAP$_{50}$ per unit of model size), we find a consistent efficiency inversion: YOLO11N achieves both the highest efficiency ($24\times$ higher than YOLO11X) and the highest absolute mAP$_{50}$ (0.617). Resolution is the dominant resource allocation lever ($+$120% efficiency gain), while additional data yields negligible returns at low resolution. These findings are robust to the deployment objective: small high-resolution configurations are Pareto-dominant across all 44 setups in the joint accuracy-throughput space, leaving no tradeoff to resolve. In data-scarce EO, bigger is not just unnecessary: it can be worse.

翻译：缩放定律假设在更多数据上训练的更大模型始终优于较小模型——这一假设驱动着计算机视觉中的模型选择，但在资源受限的地球观测领域尚未得到验证。我们在马达加斯加的屋顶光伏检测任务上，对三个缩放维度（模型大小、数据集大小和输入分辨率）进行了系统性效率分析。以模型效率（单位模型大小对应的 mAP$_{50}$）为优化目标，我们发现了一个一致的效率反转现象：YOLO11N 同时实现了最高效率（比 YOLO11X 高 $24\times$）和最高的绝对 mAP$_{50}$（0.617）。分辨率是主导的资源分配杠杆（带来 $+$120% 的效率增益），而额外的数据在低分辨率下产生的回报可忽略不计。这些发现对于部署目标具有稳健性：在联合精度-吞吐量空间中，所有 44 种设置下，小型高分辨率配置均呈现帕累托占优，不存在需要权衡的取舍。在数据稀缺的地球观测领域，更大不仅不必要，甚至可能更差。