Research output per year
Research output per year
Personal profile
Short presentation
I am currently working as a PhD fellow at the University of Copenhagen, funded by the MSCA/H2020 program. My PhD project lies at the intersection between physical geography (micrometeorology and ecophysiology), near-field remote sensing (UAV-based) and computer science. I conduct my fieldwork at two ICOS ecosystem sites to compare our C stock estimates with ecosystem-level NEE. In terms of skills, I am particularly interested in applying DL frameworks to point cloud data understanding, and time series analysis (nowcasting) applied to flux measurements. My goal is to contribute to the development of methods to accurately map biomass and C stocks from mobile platforms.
Collaborations and top research areas from the last five years
Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
Research output
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Deep point cloud regression for above-ground forest biomass estimation from airborne LiDAR
Oehmcke, S., Li, L., Trepekli, K., Revenga, J. C., Nord-Larsen, T., Gieseke, F. & Igel, C., 2024, In: Remote Sensing of Environment. 302, 21 p., 113968.Research output: Contribution to journal › Journal article › Research › peer-review
Open AccessFile7 Citations (Scopus)53 Downloads (Pure) -
Independent estimates of net carbon uptake in croplands: UAV-LiDAR and machine learning vs. eddy covariance
Revenga, J. C., Trepekli, K., Jensen, R., Rummel, P. S. & Friborg, T., 2024, In: Agricultural and Forest Meteorology. 355, 16 p., 110106.Research output: Contribution to journal › Journal article › Research › peer-review
Open AccessFile2 Downloads (Pure) -
UAV-LiDAR and machine learning methods for above-ground biomass and carbon stock prediction in forests and croplands
Caballer Revenga, J., 2023, 203 p.Research output: Book/Report › Ph.D. thesis
File -
Above-Ground Biomass Prediction for Croplands at a Sub-Meter Resolution Using UAV–LiDAR and Machine Learning Methods
Revenga, J. C., Trepekli, K., Oehmcke, S., Jensen, R., Li, L., Igel, C., Gieseke, F. C. & Friborg, T., 2022, In: Remote Sensing. 14, 16, 22 p., 3912.Research output: Contribution to journal › Journal article › Research › peer-review
Open AccessFile15 Citations (Scopus)78 Downloads (Pure) -
Deep learning based 3D point cloud regression for estimating forest biomass
Oehmcke, S., Li, L., Revenga, J. C., Nord-Larsen, T., Trepekli, K., Gieseke, F. & Igel, C., 1 Nov 2022, 30th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2022. Renz, M., Sarwat, M., Nascimento, M. A., Shekhar, S. & Xie, X. (eds.). Association for Computing Machinery, Inc., p. 1-4 38Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Open Access9 Citations (Scopus) -
Plant Structure and Carbon Storage Assessment Utilizing Drone-Borne Lidar and Deep Learning Technologies in a Danish Agricultural Expanse.
Trepekli, K., Revenga, J. C., Oehmcke, S., Gieseke, F., Jensen, R. & Friborg, T., 2021. 12 p.Research output: Contribution to conference › Paper › Research
Open AccessFile18 Downloads (Pure) -
Prediction of above ground biomass and C-stocks based on UAV-LiDAR,multispectral imagery and machine learning methods.
Revenga, J. C., Trepekli, K., Oehmcke, S., Gieseke, F., Jensen, R. & Friborg, T., 2021. 1 p.Research output: Contribution to conference › Conference abstract for conference › Research › peer-review
Open Access