Stefan Oehmcke
Adjunkt
ORCID: 0000-0002-0240-1559
1 - 3 ud af 3Pr. side: 10
- 2018
Input quality aware convolutional LSTM networks for virtual marine sensors
Oehmcke, Stefan, Zielinski, O. & Kramer, O., 31 jan. 2018, I: Neurocomputing. 275, s. 2603-2615 13 s.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
Direct training of dynamic observation noise with UMarineNet
Oehmcke, Stefan, Zielinski, O. & Kramer, O., 1 jan. 2018, Artificial Neural Networks and Machine Learning – ICANN 2018 - 27th International Conference on Artificial Neural Networks, 2018, Proceedings. Kurkova, V., Hammer, B., Manolopoulos, Y., Iliadis, L. & Maglogiannis, I. (red.). Springer Verlag, s. 123-133 11 s. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 11139 LNCS).Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
Knowledge sharing for population based neural network training
Oehmcke, Stefan & Kramer, O., 1 jan. 2018, KI 2018: Advances in Artificial Intelligence - 41st German Conference on AI, 2018, Proceedings. Turhan, A-Y. & Trollmann, F. (red.). Springer Verlag, s. 258-269 12 s. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bind 11117 LNAI).Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
ID: 209373892
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Creating cloud-free satellite imagery from image time series with deep learning
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Deep learning enables image-based tree counting, crown segmentation and height prediction at national scale
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Above-Ground Biomass Prediction for Croplands at a Sub-Meter Resolution Using UAV–LiDAR and Machine Learning Methods
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