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Found 7 papers, 0 papers with code
Exploring DINO: Emergent Properties and Limitations for Synthetic Aperture Radar Imagery
We observe a small improvement in model performance with pre-training compared to training from scratch and discuss the limitations and opportunities of SSL for remote sensing and land cover segmentation.
Exploring Generalisability of Self-Distillation with No Labels for SAR-Based Vegetation Prediction
In this work we pre-train a DINO-ViT based model using two Synthetic Aperture Radar datasets (S1GRD or GSSIC) across three regions (China, Conus, Europe).
Large Scale Masked Autoencoding for Reducing Label Requirements on SAR Data
Satellite-based remote sensing is instrumental in the monitoring and mitigation of the effects of anthropogenic climate change.
Fewshot learning on global multimodal embeddings for earth observation tasks
In this work we pretrain a CLIP/ViT based model using three different modalities of satellite imagery across five AOIs covering over ~10\% of Earth's total landmass, namely Sentinel 2 RGB optical imagery, Sentinel 1 SAR radar amplitude and interferometric coherence.
RotNet: Fast and Scalable Estimation of Stellar Rotation Periods Using Convolutional Neural Networks
We harness the power of deep learning and successfully apply Convolutional Neural Networks to regress stellar rotation periods from Kepler light curves.
Probabilistic Super-Resolution of Solar Magnetograms: Generating Many Explanations and Measuring Uncertainties
Machine learning techniques have been successfully applied to super-resolution tasks on natural images where visually pleasing results are sufficient.
Single-Frame Super-Resolution of Solar Magnetograms: Investigating Physics-Based Metrics \& Losses
Breakthroughs in our understanding of physical phenomena have traditionally followed improvements in instrumentation.
