HyperRS: Hyperbolic Embeddings for Hierarchical Aerial Image Classification

Remote sensing scenes are naturally organized into semantic hierarchies, yet existing remote sensing foundation models typically discard this structure and learn flat representations in Euclidean space. Consequently, their predictions may be accurate without reflecting the semantic relations among categories. We introduce HyperRS, a hierarchical fine-tuning framework that explicitly aligns a pretrained hyperbolic image–text model with a remote sensing taxonomy. HyperRS applies hierarchical supervised contrastive learning at every taxonomy level and introduces a ranking loss that orders category embeddings by semantic granularity, placing fine-grained categories farther from the hyperbolic origin than their ancestors. Together with an entailment objective, these losses organize both the semantic and radial structure of the representation. Experiments on Million-AID and RSI-CB256 show that HyperRS outperforms the evaluated single-level and hierarchical models, achieving hierarchical precision of $87.2\%$ and $94.9\%$, respectively. Detailed analysis shows that HyperRS preserves correct coarse-level predictions more often when fine-grained classification fails and recovers ancestor chains more faithfully through geometric traversal than the compared baselines. It also maintains higher hierarchical precision than baselines across three common remote sensing image corruptions.