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Observation-driven correction of numerical weather prediction for marine winds
Matteo Peduto, Qidong Yang, Jonathan Giezendanner +2
Accurate marine wind forecasts are essential for safe navigation, ship routing, and energy operations, yet they remain challenging because observations over the ocean are sparse, h…
MaskSDM with Shapley values to improve flexibility, robustness, and explainability in species distribution modeling
Robin Zbinden, Nina van Tiel, Gencer Sumbul +3
Species Distribution Models (SDMs) play a vital role in biodiversity research, conservation planning, and ecological niche modeling by predicting species distributions based on env…
CISO: Species Distribution Modeling Conditioned on Incomplete Species Observations
Hager Radi Abdelwahed, Mélisande Teng, Robin Zbinden +4
Species distribution models (SDMs) are widely used to predict species' geographic distributions, serving as critical tools for ecological research and conservation planning. Typica…
Better, Not Just More: Data-Centric Machine Learning for Earth Observation
Ribana Roscher, Marc RuÃwurm, Caroline Gevaert +8
Recent developments and research in modern machine learning have led to substantial improvements in the geospatial field. Although numerous deep learning architectures and models h…
What to align in multimodal contrastive learning?
Benoit Dufumier, Javiera Castillo-Navarro, Devis Tuia +1
Humans perceive the world through multisensory integration, blending the information of different modalities to adapt their behavior. Contrastive learning offers an appealing solut…
Multi-Scale and Multimodal Species Distribution Modeling
Nina van Tiel, Robin Zbinden, Emanuele Dalsasso +3
Species distribution models (SDMs) aim to predict the distribution of species by relating occurrence data with environmental variables. Recent applications of deep learning to SDMs…