1 citations · 1 across the 6 of their papers we have counts for
6 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…
: Think in Thermal Time for Generalizable Crop Mapping from Satellite Image Time Series
Mehmet Ozgur Turkoglu, Selene Ledain, Jeffrey Zweidler +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…
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…
Towards Robust Deep Learning-based Rumex Obtusifolius Detection from Drone Images
Fabian Dionys Schrag, Mehmet Ozgur Turkoglu, Konrad Schindler +1
Domain adaptation (DA) addresses the challenge of transferring a machine learning model trained on a source domain to a target domain with a different data distribution. In this wo…
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…