Stefan Oehmcke

Assistant Professor

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ORCID: 0000-0002-0240-1559

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2024
Published
Deep point cloud regression for above-ground forest biomass estimation from airborne LiDAR
Oehmcke, Stefan, Li, Lei, Trepekli, Aikaterini, Caballer Revenga, Jaime, Nord-Larsen, Thomas, Gieseke, Fabian Cristian & Igel, Christian, 2024, In: Remote Sensing of Environment. 302, 21 p., 113968.
Research output: Contribution to journal › Journal article › Research › peer-review
Published
Scattered tree death contributes to substantial forest loss in California
Cheng, Yan, Oehmcke, Stefan, Brandt, Martin Stefan, Rosenthal, L., Das, A., Vrieling, A., Saatchi, S., Wagner, F., Mugabowindekwe, Maurice, Verbruggen, Wim, Beier, Claus & Horion, Stéphanie, 2024, In: Nature Communications. 15, 1, p. 1-13 641.
Research output: Contribution to journal › Journal article › Research › peer-review

ID: 209373892

119 downloads
Creating cloud-free satellite imagery from image time series with deep learning
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Published
106 downloads
Deep learning enables image-based tree counting, crown segmentation and height prediction at national scale
Research output: Contribution to journal › Journal article › Research › peer-review
Published
69 downloads
Above-Ground Biomass Prediction for Croplands at a Sub-Meter Resolution Using UAV–LiDAR and Machine Learning Methods
Research output: Contribution to journal › Journal article › Research › peer-review
Published