| Attentional feature fusion Y Dai, F Gieseke, S Oehmcke, Y Wu, K Barnard Proceedings of the IEEE/CVF winter conference on applications of computer …, 2021 | 591 | 2021 |
| Input quality aware convolutional LSTM networks for virtual marine sensors S Oehmcke, O Zielinski, O Kramer Neurocomputing 275, 2603-2615, 2018 | 56 | 2018 |
| kNN ensembles with penalized DTW for multivariate time series imputation S Oehmcke, O Zielinski, O Kramer 2016 International Joint Conference on Neural Networks (IJCNN), 2774-2781, 2016 | 49 | 2016 |
| More than one quarter of Africa’s tree cover is found outside areas previously classified as forest F Reiner, M Brandt, X Tong, D Skole, A Kariryaa, P Ciais, A Davies, ... Nature Communications 14 (1), 2258, 2023 | 35 | 2023 |
| Event detection in marine time series data S Oehmcke, O Zielinski, O Kramer KI 2015: Advances in Artificial Intelligence: 38th Annual German Conference …, 2015 | 34 | 2015 |
| Deep learning based 3D point cloud regression for estimating forest biomass S Oehmcke, L Li, JC Revenga, T Nord-Larsen, K Trepekli, F Gieseke, ... Proceedings of the 30th International Conference on Advances in Geographic …, 2022 | 24 | 2022 |
| Detecting hardly visible roads in low-resolution satellite time series data S Oehmcke, C Thrysøe, A Borgstad, MAV Salles, M Brandt, F Gieseke 2019 IEEE international conference on big data (big data), 2403-2412, 2019 | 24 | 2019 |
| Above-ground biomass prediction for croplands at a sub-meter resolution using uav–lidar and machine learning methods JC Revenga, K Trepekli, S Oehmcke, R Jensen, L Li, C Igel, FC Gieseke, ... Remote Sensing 14 (16), 3912, 2022 | 18 | 2022 |
| Creating cloud-free satellite imagery from image time series with deep learning S Oehmcke, THK Chen, AV Prishchepov, F Gieseke Proceedings of the 9th ACM SIGSPATIAL International Workshop on Analytics …, 2020 | 14 | 2020 |
| Deep learning enables image-based tree counting, crown segmentation, and height prediction at national scale S Li, M Brandt, R Fensholt, A Kariryaa, C Igel, F Gieseke, T Nord-Larsen, ... PNAS nexus 2 (4), pgad076, 2023 | 13 | 2023 |
| Evolution of stacked autoencoders T Silhan, S Oehmcke, O Kramer 2019 IEEE Congress on Evolutionary Computation (CEC), 823-830, 2019 | 12 | 2019 |
| LR-CSNet: low-rank deep unfolding network for image compressive sensing T Zhang, L Li, C Igel, S Oehmcke, F Gieseke, Z Peng 2022 IEEE 8th International Conference on Computer and Communications (ICCC …, 2022 | 11 | 2022 |
| Spatio-temporal wind power prediction using recurrent neural networks WL Woon, S Oehmcke, O Kramer Neural Information Processing: 24th International Conference, ICONIP 2017 …, 2017 | 10 | 2017 |
| BuildSeg: a general framework for the segmentation of buildings L Li, T Zhang, S Oehmcke, F Gieseke, C Igel arXiv preprint arXiv:2301.06190, 2023 | 9 | 2023 |
| Storyteller: in-situ reflection on study experiences B Poppinga, S Oehmcke, W Heuten, S Boll Proceedings of the 15th international conference on Human-computer …, 2013 | 9 | 2013 |
| Multi-scale pseudo labeling for unsupervised deep edge detection C Zhou, C Yuan, H Wang, L Li, S Oehmcke, J Liu, J Peng Knowledge-Based Systems 280, 111057, 2023 | 8 | 2023 |
| Attention as activation Y Dai, S Oehmcke, F Gieseke, Y Wu, K Barnard 2020 25th International Conference on Pattern Recognition (ICPR), 9156-9163, 2021 | 8 | 2021 |
| Recurrent neural networks and exponential PAA for virtual marine sensors S Oehmcke, O Zielinski, O Kramer 2017 International Joint Conference on Neural Networks (IJCNN), 4459-4466, 2017 | 8 | 2017 |
| Remember to correct the bias when using deep learning for regression! C Igel, S Oehmcke KI-Künstliche Intelligenz 37 (1), 33-40, 2023 | 7 | 2023 |
| Analysis of diversity methods for evolutionary multi-objective ensemble classifiers S Oehmcke, J Heinermann, O Kramer Applications of Evolutionary Computation: 18th European Conference …, 2015 | 7 | 2015 |
Fabian GiesekeDepartment of Information Systems, University of MünsterVerified email at uni-muenster.de
