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cs.CV2026
Depth-Guided Contrastive Learning for 2D Representations with 3D Spatial Awareness
Liang Zeng, Maarten Vergauwen
Standard contrastive learning frameworks are mainly designed from a semantic perspective, yet learning 2D visual representations that preserve 3D spatial structure is also importan…
cs.CV2026
NeighborMAE: Exploiting Spatial Dependencies between Neighboring Earth Observation Images in Masked Autoencoders Pretraining
Liang Zeng, Valerio Marsocci, Wufan Zhao +2
Masked Image Modeling has been one of the most popular self-supervised learning paradigms to learn representations from large-scale, unlabeled Earth Observation images. While incor…
cs.CV2025
Can Generative Geospatial Diffusion Models Excel as Discriminative Geospatial Foundation Models?
Yuru Jia, Valerio Marsocci, Ziyang Gong +3
Self-supervised learning (SSL) has revolutionized representation learning in Remote Sensing (RS), advancing Geospatial Foundation Models (GFMs) to leverage vast unlabeled satellite…