15 citations · 19 across the 11 of their papers we have counts for
14 papers
Reuse or Relearn? A Spectral View of Earth Observation Foundation Models
Mehmet Ozgur Turkoglu, Valerio Marsocci, Dominik J. Mühlematter +3
Foundation models are rarely used as generic, frozen feature extractors; instead, they are fine-tuned for the target downstream application. This practice is particularly prevalent…
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…
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…
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…
FiLM-Ensemble: Probabilistic Deep Learning via Feature-wise Linear Modulation
Mehmet Ozgur Turkoglu, Alexander Becker, Hüseyin Anil Gündüz +6
The ability to estimate epistemic uncertainty is often crucial when deploying machine learning in the real world, but modern methods often produce overconfident, uncalibrated uncer…
Urban Change Forecasting from Satellite Images
Nando Metzger, Mehmet Özgür Türkoglu, Rodrigo Caye Daudt +2
Forecasting where and when new buildings will emerge is a rather unexplored topic, but one that is very useful in many disciplines such as urban planning, agriculture, resource man…
Visual Camera Re-Localization Using Graph Neural Networks and Relative Pose Supervision
Mehmet Ozgur Turkoglu, Eric Brachmann, Konrad Schindler +2
Visual re-localization means using a single image as input to estimate the camera's location and orientation relative to a pre-recorded environment. The highest-scoring methods are…
Crop mapping from image time series: deep learning with multi-scale label hierarchies
Mehmet Ozgur Turkoglu, Stefano D'Aronco, Gregor Perich +4
The aim of this paper is to map agricultural crops by classifying satellite image time series. Domain experts in agriculture work with crop type labels that are organised in a hier…
Crop Classification under Varying Cloud Cover with Neural Ordinary Differential Equations
Nando Metzger, Mehmet Ozgur Turkoglu, Stefano D'Aronco +2
Optical satellite sensors cannot see the Earth's surface through clouds. Despite the periodic revisit cycle, image sequences acquired by Earth observation satellites are therefore…
Gating Revisited: Deep Multi-layer RNNs That Can Be Trained
Mehmet Ozgur Turkoglu, Stefano D'Aronco, Jan Dirk Wegner +1
We propose a new STAckable Recurrent cell (STAR) for recurrent neural networks (RNNs), which has fewer parameters than widely used LSTM and GRU while being more robust against vani…
A Layer-Based Sequential Framework for Scene Generation with GANs
Mehmet Ozgur Turkoglu, William Thong, Luuk Spreeuwers +1
The visual world we sense, interpret and interact everyday is a complex composition of interleaved physical entities. Therefore, it is a very challenging task to generate vivid sce…