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Found 5 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.
AI applications in forest monitoring need remote sensing benchmark datasets
We present pragmatic requirements and considerations for the creation of rigorous, useful benchmarking datasets for forest monitoring applications, and discuss how tools from modern data science can improve use of existing data.
