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Found 5 papers, 4 papers with code
PixelDINO: Semi-Supervised Semantic Segmentation for Detecting Permafrost Disturbances
To improve model generalization across the Arctic without the need for additional labelled data, we present a semi-supervised learning approach to train semantic segmentation models to detect RTS.
A Deep Active Contour Model for Delineating Glacier Calving Fronts
Building on this observation, we completely rephrase the task as a contour tracing problem and propose a model for explicit contour detection that does not incorporate any dense predictions as intermediate steps.
Self-supervised Audiovisual Representation Learning for Remote Sensing Data
By fine-tuning the models on a number of commonly used remote sensing datasets, we show that our approach outperforms existing pre-training strategies for remote sensing imagery.
Ranked #2 on
Cross-Modal Retrieval
on SoundingEarth
Aerial Scene Understanding in The Wild: Multi-Scene Recognition via Prototype-based Memory Networks
To be more specific, we first learn the prototype representation of each aerial scene from single-scene aerial image datasets and store it in an external memory.
HED-UNet: Combined Segmentation and Edge Detection for Monitoring the Antarctic Coastline
Deep learning-based coastline detection algorithms have begun to outshine traditional statistical methods in recent years.
