Stefan Oehmcke

1.8k citations

26 papers · 993 indexed · 1 hit paper · h-index 10

Computer Vision and Pattern Recognition top 2%
- Advanced Neural Network Applications 4
Media Technology top 2%
- Advanced Image Fusion Techniques 2
Environmental Engineering top 5%
- Remote Sensing and LiDAR Applications 7
Signal Processing top 10%
- Time Series Analysis and Forecasting 3
Artificial Intelligence top 5%
- Anomaly Detection Techniques and Applications 5
- Neural Networks and Applications 3
Ecology
- Remote Sensing in Agriculture 5
Nature and Landscape Conservation
- Forest ecology and management 5

Co-authors: Fabian Gieseke Yiquan Wu Kobus Barnard Yimian Dai Oliver Krämer Oliver Zielinski Christian IgelLei Li
Cited by: Computer Vision and Pattern Recognition Media Technology Environmental Engineering
Journals: Remote Sensing of Environment (2 papers)EPJ Data Science (1 paper)Remote Sensing (1 paper)
Partner nations: Denmark Germany China

In The Last Decade

Stefan Oehmcke

26 papers receiving 976 citations

Hit Papers

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Peers

Countries citing papers authored by Stefan Oehmcke

Since Specialization

Citations

This map shows the geographic impact of Stefan Oehmcke's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Stefan Oehmcke with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Stefan Oehmcke more than expected).

Fields of papers citing papers by Stefan Oehmcke

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Stefan Oehmcke. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Stefan Oehmcke. The network helps show where Stefan Oehmcke may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Stefan Oehmcke, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Stefan Oehmcke Line = papers co-authored together Stefan Oehmcke links everyone, so they are left out of the graph.

All Works

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20 of 20 papers shown

#	Work
1	Predictability of abrupt shifts in dryland ecosystem functioning Nature Climate Change ·이대규,Wanda De Keersmaecker,Stéphanie Horion,Stefan Oehmcke,Fabian Gieseke,Rasmus Fensholt,Ruben Van De Kerchove,Stef Lhermitte,Christin Abel,Koenraad Van Meerbeek,Jan Verbesselt,Ben Somers	2025	8
2	Global mapping of human-transformed dike-pond systems Remote Sensing of Environment ·Yang Xu,Lian Feng,Hongwei Fang,Xiao‐Peng Song,Fabian Gieseke,Ankit Kariryaa,Stefan Oehmcke,Luke Gibson,A.M. Devine,Бирюков Михаил Иванович,R. Iestyn Woolway,Chunmiao Zheng,Martin Brandt,Rasmus Fensholt	2024	10
3	Scattered tree death contributes to substantial forest loss in California Nature Communications ·William V. Andresen,Stefan Oehmcke,Martin Brandt,Lisa Rosenthal,Adrian J. Das,Anton Vrieling,Sassan Saatchi,Fabien Wagner,Maurice Mugabowindekwe,이균성,Claus Beier,Stéphanie Horion	2024	19
4	Deep point cloud regression for above-ground forest biomass estimation from airborne LiDAR Remote Sensing of Environment ·Stefan Oehmcke,Lei Li,Susan Salubuyi,Anni Zeynalvand,Thomas Nord‐Larsen,Fabian Gieseke,Christian Igel	2024	29
5	High nitrous oxide emissions from temporary flooded depressions within croplands Communications Earth & Environment ·Bo Elberling,Zhao Hai-n,Noorma Kurnyawaty,Rasmus Fensholt,Per Ambus,Xiaoye Tong,М. С. Лебедкина,Carsten W. Mueller,F. B. FIORINI,Stefan Oehmcke	2023	6
6	BuildSeg: A General Framework for the Segmentation of Buildings Eman ًWanis,Tianfang Zhang,Stefan Oehmcke,Fabian Gieseke,Christian Igel	2023	4
7	Deep learning enables image-based tree counting, crown segmentation, and height prediction at national scale PNAS Nexus ·Sizhuo Li,Martin Brandt,Rasmus Fensholt,Ankit Kariryaa,Christian Igel,Fabian Gieseke,Thomas Nord‐Larsen,Stefan Oehmcke,T. Stubb,Samuli Junttila,Xiaoye Tong,Alexandre d’Aspremont,Philippe Ciais	2023	37
8	Seasonal-Trend Time Series Decomposition on Graphics Processing Units Socio-Environmental Systems Modeling ·Agnieszka Dżaluk,Stefan Oehmcke,Cosmin E. Oancea,Dainius Masiliū̄nas,Jan Verbesselt,William V. Andresen,Stéphanie Horion,Fabian Gieseke,V. Dubenskyy	2023	1
9	Remember to Correct the Bias When Using Deep Learning for Regression! KI - Künstliche Intelligenz ·Christian Igel,Stefan Oehmcke	2023	4
10	Multi-scale pseudo labeling for unsupervised deep edge detection Knowledge-Based Systems ·Changsheng Zhou,Chao Yuan,Hongxin Wang,Lei Li,Stefan Oehmcke,Junmin Liu,Jigen Peng	2023	3
11	Above-Ground Biomass Prediction for Croplands at a Sub-Meter Resolution Using UAV–LiDAR and Machine Learning Methods Remote Sensing ·Anni Zeynalvand,Susan Salubuyi,Stefan Oehmcke,Rasmus Jensen,Lei Li,Christian Igel,Fabian Gieseke,Thomas Friborg	2022	22
12	Prediction of above ground biomass and C-stocks based on UAV-LiDAR,multispectral imagery and machine learning methods. Research at the University of Copenhagen (University of Copenhagen) ·Anni Zeynalvand,Susan Salubuyi,Stefan Oehmcke,Fabian Gieseke,Rasmus Jensen,Thomas Friborg	2021	2
13	Attention as Activation Research at the University of Copenhagen (University of Copenhagen) ·Yimian Dai,Stefan Oehmcke,Fabian Gieseke,Yiquan Wu,Kobus Barnard	2021	6
14	Both sides of the story: comparing student-level data on reading performance from administrative registers to application generated data from a reading app EPJ Data Science ·K.A. Bousdras,Emil L. Smith,David Reimer,Stefan Oehmcke,光章鐘撞	2021	2
15	Estimating Forest Canopy Height With Multi-Spectral and Multi-Temporal Imagery Using Deep Learning 2021 IEEE International Conference on Big Data (Big Data) ·Stefan Oehmcke,Vazir R,Kenneth Grogan,Fabian Gieseke	2021	1
16	Evolution of Stacked Autoencoders Research at the University of Copenhagen (University of Copenhagen) ·D. J. Partello,Stefan Oehmcke,Oliver Krämer	2019	5
17	Input quality aware convolutional LSTM networks for virtual marine sensors Neurocomputing ·Stefan Oehmcke,Oliver Zielinski,Oliver Krämer	2017	51
18	Recurrent neural networks and exponential PAA for virtual marine sensors Stefan Oehmcke,Oliver Zielinski,Oliver Krämer	2017	3
19	kNN ensembles with penalized DTW for multivariate time series imputation Stefan Oehmcke,Oliver Zielinski,Oliver Krämer	2016	34
20	Storyteller Benjamin Poppinga,Stefan Oehmcke,Wilko Heuten,Susanne Boll	2013	3

About Stefan Oehmcke

Stefan Oehmcke is a scholar working on Environmental Engineering, Nature and Landscape Conservation, Signal Processing, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 26 papers that have together received 993 indexed citations. Recurring topics across this work include Remote Sensing and LiDAR Applications (7 papers), Remote Sensing in Agriculture (5 papers), Forest ecology and management (5 papers), Anomaly Detection Techniques and Applications (5 papers), Advanced Neural Network Applications (4 papers), Time Series Analysis and Forecasting (3 papers), Neural Networks and Applications (3 papers) and Advanced Image Fusion Techniques (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (402 citations), Media Technology (139 citations), Environmental Engineering (158 citations), Signal Processing (80 citations) and Artificial Intelligence (225 citations). Stefan Oehmcke has collaborated with scholars based in Denmark, Germany and China. Frequent co-authors include Fabian Gieseke, Yiquan Wu, Kobus Barnard, Yimian Dai, Oliver Krämer, Oliver Zielinski, Christian Igel, Lei Li, Thomas Nord‐Larsen and Rasmus Fensholt. Their work appears in journals such as Remote Sensing of Environment, EPJ Data Science, Remote Sensing, Nature Climate Change and Communications Earth & Environment.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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