5 papers
Learning to Forecast Crop Growth from Earth Observation Data
Dominik Senti, Mehmet Ozgur Turkoglu, Michele Volpi +1
Forecasting crop growth across agricultural landscapes is important for improving the productivity, resilience, and operational management of farming systems. In this work, we inve…
SwissCrop25: A National Multi-Year Benchmark for Operational Crop Mapping
Thomas Lauber, Mehmet Ozgur Turkoglu, Sélène Ledain +1
Operational crop mapping requires models that generalise across years, resolve fine-grained crop taxonomies, and distinguish cropland from surrounding landscapes. However, existing…
Quantifying the Uncertainty of Foundation Models with Singular Value Ensembles
Mehmet Ozgur Turkoglu, Dominik J. Mühlematter, Alexander Becker +2
Foundation models have become a dominant paradigm in machine learning, achieving remarkable performance across diverse tasks through large-scale pretraining. However, they often yi…
: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series
Mehmet Ozgur Turkoglu, Selene Ledain, Thomas Lauber +2
Crop type classification from optical satellite time series remains limited in its ability to generalize across growing seasons, particularly when crop phenology shifts due to inte…
LoRA-Ensemble: Efficient Uncertainty Modelling for Self-Attention Networks
Dominik J. Mühlematter, Michelle Halbheer, Alexander Becker +4
Numerous real-world decisions rely on machine learning algorithms and require calibrated uncertainty estimates. However, modern methods often yield overconfident, uncalibrated pred…