SkySplat-OV: Generalizable Language Gaussian Splatting for Open-Vocabulary Scene Understanding from Sparse Satellite Views

Open-vocabulary scene understanding aims to recognize ar-bitrary semantic concepts in 3D beyond a fixed set of categories. Re-cent methods built upon 3D Gaussian Splatting (3DGS) enable sceneunderstanding by distilling features from vision–language models, butmost rely on per-scene optimization and dense observations, which limitscalability. More recent generalizable 3DGS methods show promise, butthey perform poorly on sparse-view satellite imagery due to the uniquepushbroom imaging mode, limited geometric constraints, and extremescale variations. To address these limitations, we propose SkySplat-OV,a feed-forward framework that integrates the rational polynomial co-efficient (RPC) model into a generalizable language 3DGS pipeline. AGeometry–Radiance–Semantics Fusion module, together with monoc-ular relative height supervision, improves Gaussian parameter predic-tion under limited geometric constraints. Moreover, SkySplat-OV em-ploys single-stage SAM3 distillation to handle extreme scale variationsin satellite imagery for accurate segmentation. Compared to per-sceneoptimization methods, SkySplat-OV achieves up to 1070 times speedupover LangSplatV2 with higher accuracy. It also outperforms generaliz-able 3DGS baselines, achieving 12.91%–25.75% mIoU improvements ontwo benchmarks, and demonstrates strong cross-dataset generalizationon the MVS3D test set. Additional qualitative results are available athttps://skysplat-ov.github.io/.