21 citations · 32 across the 6 of their papers we have counts for
7 papers
SAGE: A sampling-aware global evaluation benchmark for species distribution modeling
Emilia Arens, Nina van Tiel, Robin Zbinden +8
Knowing where species occur is fundamental for biodiversity research and conservation. Species distribution models (SDMs) link species observations to environmental conditions to e…
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…
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…
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…
Imbalance-aware Presence-only Loss Function for Species Distribution Modeling
Robin Zbinden, Nina van Tiel, Marc Rußwurm +1
In the face of significant biodiversity decline, species distribution models (SDMs) are essential for understanding the impact of climate change on species habitats by connecting e…
On the selection and effectiveness of pseudo-absences for species distribution modeling with deep learning
Robin Zbinden, Nina van Tiel, Benjamin Kellenberger +2
Species distribution modeling is a highly versatile tool for understanding the intricate relationship between environmental conditions and species occurrences. However, the availab…
Geographic Location Encoding with Spherical Harmonics and Sinusoidal Representation Networks
Marc Rußwurm, Konstantin Klemmer, Esther Rolf +2
Learning representations of geographical space is vital for any machine learning model that integrates geolocated data, spanning application domains such as remote sensing, ecology…