activity
20192026
most citedFiLM-Ensemble: Probabilistic Deep Learning via Feature-wise Linear Modulation

15 citations · 19 across the 11 of their papers we have counts for

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10 papers · 1 filter

cs.CV2026

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…

cs.CV2026

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…

cs.CV2026

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…

cs.CV2025

: 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…

cs.CV2022

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…

cs.CV2021

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…

cs.CV2021★ 1 cited

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…

cs.CV2020★ 1 cited

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…

cs.CV2019

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…

cs.CV2019★ 1 cited

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…