most citedFoundation Models in Remote Sensing: Evolving from Unimodality to Multimodality

6 citations · 6 across the 3 of their papers we have counts for

collaborators

7 papers

cs.CE2026

Generative modeling of granular flow on inclined planes using conditional flow matching

Xuyang Li, Rui Li, Teng Man +1

Granular flows govern many natural and industrial processes, yet their interior kinematics and mechanics remain largely unobservable, as experiments access only boundaries or free…

cs.CV2026

CLIP-Guided Data Augmentation for Night-Time Image Dehazing

Xining Ge, Weijun Yuan, Gengjia Chang +2

Nighttime image dehazing faces a more complex degradation pattern than its daytime counterpart, as haze scattering couples with low illumination, non-uniform lighting, and strong l…

cs.CV20266 cited

Foundation Models in Remote Sensing: Evolving from Unimodality to Multimodality

Danfeng Hong, Chenyu Li, Xuyang Li +2

Remote sensing (RS) techniques are increasingly crucial for deepening our understanding of the planet. As the volume and diversity of RS data continue to grow exponentially, there…

cs.CV2026

STARS: Shared-specific Translation and Alignment for missing-modality Remote Sensing Semantic Segmentation

Tong Wang, Xiaodong Zhang, Guanzhou Chen +7

Multimodal remote sensing technology significantly enhances the understanding of surface semantics by integrating heterogeneous data such as optical images, Synthetic Aperture Rada…

cs.CV2025

Any-Optical-Model: A Universal Foundation Model for Optical Remote Sensing

Xuyang Li, Chenyu Li, Danfeng Hong

Optical satellites, with their diverse band layouts and ground sampling distances, supply indispensable evidence for tasks ranging from ecosystem surveillance to emergency response…

cs.CV2025

SeaMo: A Season-Aware Multimodal Foundation Model for Remote Sensing

Xuyang Li, Chenyu Li, Gemine Vivone +1

Remote Sensing (RS) data encapsulates rich multi-dimensional information essential for Earth observation. Its vast volume, diverse sources, and temporal continuity make it particul…