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.

With the extensive application of big spatial data and the emergence of spatial computing, augmented reality (AR) map rendering has attracted significant attention. A common issue in existing solutions is that AR-GIS systems rely on different platform-specific graphics libraries on different operating systems, and rendering implementations can vary across various platforms. This causes performance degradation and rendering styles that are not consistent across environments. However, high-performance rendering consistency across devices is critical in AR-GIS, especially for edge collaborative computing. In this paper, we present a high-performance, platform-independent AR-GIS rendering engine; the augmented reality universal graphics library (AUGL) engine. A unified cross-platform interface is proposed to preserve AR-GIS rendering style consistency across platforms. High-performance AR-GIS map symbol drawing models are defined and implemented based on a unified algorithm interface. We also develop a pre-caching strategy, optimized spatial-index querying, and a GPU-accelerated vector drawing algorithm that minimizes IO latency throughout the rendering process. Comparisons to existing AR-GIS visualization engines indicate that the performance of the AUGL engine is two times higher than that of the AR-GIS rendering engine on the Android, iOS, and Vuforia platforms. The drawing efficiency for vector polygons is improved significantly. The rendering performance is more than three times better than the average performances of existing Android and iOS systems.