HyperR3SNet: Leveraging Hyperbolic Space and Vision Foundation Models for Remote Sensing Semantic Segmentation

Remote-sensing semantic segmentation drives by land-use monitoring, urban planning, and ecological assessment, yet progress is hampered by scarce pixel-level labels. To address this issue, we present HyperR3SNet, which is an efficient framework for remote-sensing semantic segmentation that tackles data scarcity and scale variations in overhead imagery. HyperR3SNet transfers self-supervised vision foundation models to remote sensing, providing strong feature generalisation with minimal labeled data. Building on this, a multi-scale cross-attention module is inserted into the backbone, vision transformer layer, enabling the network to extract richer features across widely varying object scales. To keep the model lightweight, a matrix-factorised task head is employed, sharply reducing parameters and computation while sustaining accuracy. HyperR3SNet is the first model to integrate a hyperbolic pixel-level loss with vision foundation model adaptation for cross-domain and low-annotation remote sensing segmentation, leveraging the exponential expansion property of hyperbolic geometry to capture latent inter-class relations and preserve structural consistency under weak supervision. Evaluated on the widely adopted remote sensing segmentation datasets (e.g., iSAID, LoveDA, Potsdam, and Vaihingen), HyperR3SNet achieves mean IoUs of 67.60 %, 55.86 %, 80.07 %, and 96.46 %, respectively, and on average surpasses a broad range of state-of-the-art methods.