arXiv Machine Learning
GeoCrossBench: Cross-Band Generalization for Remote Sensing
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arXiv Computer Vision
Sep 11
SPECTRA: Band-Routed Embedding and Stage-Wise LoRA for Cross-Sensor Fine-Tuning of Geospatial Foundation Models
SPECTRA is a parameter‑efficient fine‑tuning framework for geospatial foundation models that tackles two key challenges: spectral mismatch and high adaptation cost. It introduces Band‑Routed Embedding (BRE) to map downstream sensor bands into the pretrained model’s expected band space, enabling full use of available spectral data without altering the patch embedding interface. Additionally, Stage‑wise Transferability‑aware LoRA (ST‑LoRA) estimates stage‑wise transferability and assigns LoRA ranks accordingly, concentrating trainable parameters on the most transferable stages and reducing overall adaptation cost.
arXiv Computer Vision
1d ago
Cryo-Bench: Benchmarking Foundation Models for Cryosphere Mapping
Cryo-Bench is a new benchmark that evaluates foundation models for cryosphere mapping, comprising six semantic‑segmentation datasets across five cryospheric components (supraglacial debris, glacial lakes, sea ice, calving fronts, and Antarctic ice‑shelf extent). The benchmark includes multispectral, RGB, and SAR observations from under‑represented regions and tests thirteen geo‑foundation models alongside U‑Net and Vision Transformer baselines. Results show that with frozen encoders U‑Net slightly outperforms TerraMind, but the difference is not statistically significant; fine‑tuning with learning‑rate optimization can dramatically improve performance for some models, while in few‑shot scenarios several foundation models retain over 90 % of their full‑label accuracy.
arXiv AI
Jun 15
How do Self-Supervised Remote Sensing Vision Models Transfer to Downstream Tasks?
arXiv:2606. 13896v1 Announce Type: cross Abstract: Self-supervised geospatial foundation models (GeoFMs) learn transferable representations from remote sensing data, but their downstream behavior is difficult to characterize.
arXiv Machine Learning
Aug 21
Far from the Crowd: Scalable Self-Supervised Learning via Geographic Isolation
arXiv:2608. 19766v1 Announce Type: cross Abstract: Self-supervised pretraining on remote sensing imagery typically treats all samples as equally informative, despite large variability in geographic and visual structure.
arXiv Machine Learning
Aug 26
SatDL: Jointly Optimizing Data Redistribution and Training for Satellite-Based Distributed Learning
arXiv:2608.24516v1 Announce Type: cross Abstract: Satellite-based distributed learning promises to train machine-learning models directly in orbit using massive, globally dispersed sensor data, there...
Hugging Face Trending Papers
Jul 17
BCG-Former: Toward Pareto-Efficient Hyperspectral Image Classification via Band-Contextual Gating
Hyperspectral image (HSI) classification systems are increasingly deployed on platforms with strict computational budgets, such as UAVs and small spaceborne sensors. In these settings, accuracy alone is not enough; the model must also run within tight latency and memory constraints.