359 citations · 774 across the 13 of their papers we have counts for
8 papers · 1 filter
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…
Better, Not Just More: Data-Centric Machine Learning for Earth Observation
Ribana Roscher, Marc Rußwurm, 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…
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…
Meta-Learning for Few-Shot Land Cover Classification
Marc Rußwurm, Sherrie Wang, Marco Körner +1
The representations of the Earth's surface vary from one geographic region to another. For instance, the appearance of urban areas differs between continents, and seasonality influ…
Self-attention for raw optical Satellite Time Series Classification
Marc Rußwurm, Marco Körner
The amount of available Earth observation data has increased dramatically in the recent years. Efficiently making use of the entire body information is a current challenge in remot…
Early Classification for Agricultural Monitoring from Satellite Time Series
Marc Rußwurm, Romain Tavenard, Sébastien Lefèvre +1
In this work, we introduce a recently developed early classification mechanism to satellite-based agricultural monitoring. It augments existing classification models by an addition…
BreizhCrops: A Time Series Dataset for Crop Type Mapping
Marc Rußwurm, Charlotte Pelletier, Maximilian Zollner +2
We present Breizhcrops, a novel benchmark dataset for the supervised classification of field crops from satellite time series. We aggregated label data and Sentinel-2 top-of-atmosp…
End-to-End Learned Early Classification of Time Series for In-Season Crop Type Mapping
Marc Rußwurm, Nicolas Courty, Rémi Emonet +3
Remote sensing satellites capture the cyclic dynamics of our Planet in regular time intervals recorded in satellite time series data. End-to-end trained deep learning models use th…