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Found 10 papers, 8 papers with code
Probabilistic Machine Learning for Noisy Labels in Earth Observation
In this study, we take a step in this direction by leveraging probabilistic ML to model input-dependent label noise and quantify data uncertainty in EO tasks, accounting for the unique noise sources inherent in the domain.
Towards a Unified Copernicus Foundation Model for Earth Vision
Advances in Earth observation (EO) foundation models have unlocked the potential of big satellite data to learn generic representations from space, benefiting a wide range of downstream applications crucial to our planet.
On the Generalization of Representation Uncertainty in Earth Observation
Recent advances in Computer Vision have introduced the concept of pretrained representation uncertainty, enabling zero-shot uncertainty estimation.
Seasonal Fire Prediction using Spatio-Temporal Deep Neural Networks
With climate change expected to exacerbate fire weather conditions, the accurate anticipation of wildfires on a global scale becomes increasingly crucial for disaster mitigation.
FoMo-Bench: a multi-modal, multi-scale and multi-task Forest Monitoring Benchmark for remote sensing foundation models
Inspired by the rise of foundation models for computer vision and remote sensing, we here present the first unified Forest Monitoring Benchmark (FoMo-Bench).
Kuro Siwo: 33 billion $m^2$ under the water. A global multi-temporal satellite dataset for rapid flood mapping
Global floods, exacerbated by climate change, pose severe threats to human life, infrastructure, and the environment.
TeleViT: Teleconnection-driven Transformers Improve Subseasonal to Seasonal Wildfire Forecasting
To achieve such accurate long-term forecasts at a global scale, it is crucial to employ models that account for the Earth system's inherent spatio-temporal interactions, such as memory effects and teleconnections.
Hephaestus: A large scale multitask dataset towards InSAR understanding
Synthetic Aperture Radar (SAR) data and Interferometric SAR (InSAR) products in particular, are one of the largest sources of Earth Observation data.
Self-supervised Contrastive Learning for Volcanic Unrest Detection
Ground deformation measured from Interferometric Synthetic Aperture Radar (InSAR) data is considered a sign of volcanic unrest, statistically linked to a volcanic eruption.
Learning from Synthetic InSAR with Vision Transformers: The case of volcanic unrest detection
We report detection accuracy that amounts to the highest reported accuracy on a large test set for volcanic unrest detection.
