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cs.CV2026

EO-VGGT: Orbital Ray-Conditioned 3D Foundation Models for Satellite Multi-View Reconstruction

Qiyan Luo, Yingdong Pi, Lekang Wen +4

In the era of satellite constellations, multi-view optical satellite imagery is pivotal for Earth Observation (EO) and high-quality Digital Surface Model (DSM) reconstruction. Alth…

cs.CV2026

Heterogeneous SAR-optical fusion for near-real-time land use and land cover mapping under cloud contamination: A novel framework and global benchmark dataset

Jiangong Xu, Weibao Xue, Xiaoyu Yu +3

Optical remote sensing imagery is frequently degraded by cloud and cloud-shadow contamination, which limits its reliability for near-real-time land use and land cover (LULC) mappin…

cs.CV2026

Geometric Consistency Protocol for Foundation Model Features in Multi-View Satellite Imagery

Qiyan Luo, Jie Yang, Yingdong Pi +2

Standardized evaluation protocols are indispensable for robust benchmarking in remote sensing, particularly as foundation features are increasingly transferred across diverse senso…

cs.CV2026

GeoMamba: A Geometry-driven MambaVision Framework and Dataset for Fine-grained Optical-SAR Object Retrieval

Tiantong Fang, Xiuwei Wang, Jing Xiao +3

Multi-source remote sensing enables complementary observation of ground objects, while cross-modal fine-grained object retrieval remains challenging, especially under unaligned opt…

cs.CV2026

Open-Vocabulary Semantic Segmentation Network Integrating Object-Level Label and Scene-Level Semantic Features for Multimodal Remote Sensing Images

Jinkun Dai, Yuanxin Ye, Peng Tang +4

Semantic segmentation of multi-modal remote sensing imagery plays a pivotal role in land use/land cover (LULC) mapping, environmental monitoring, and precision earth observation. C…

cs.CV2026

Observe Less, Understand More: Cost-aware Cross-scale Observation for Remote Sensing Understanding

Zhenghao Xie, Jing Xiao, Zhenqi Wang +5

Remote sensing understanding inherently requires multi-resolution observation, since different targets and application tasks demand different levels of spatial detail. While low-re…