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Timo Tjaden Stomberg
Timo Tjaden Stomberg
PhD Student, University of Bonn
Verified email at uni-bonn.de
Title
Cited by
Cited by
Year
AQ-Bench: a benchmark dataset for machine learning on global air quality metrics
C Betancourt, T Stomberg, R Roscher, MG Schultz, S Stadtler
Earth System Science Data 13 (6), 3013-3033, 2021
252021
Global, high-resolution mapping of tropospheric ozone–explainable machine learning and impact of uncertainties
C Betancourt, TT Stomberg, AK Edrich, A Patnala, MG Schultz, R Roscher, ...
Geoscientific Model Development 15 (11), 4331-4354, 2022
242022
Jungle-net: Using explainable machine learning to gain new insights into the appearance of wilderness in satellite imagery
T Stomberg, I Weber, M Schmitt, R Roscher
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information …, 2021
212021
MapInWild: A remote sensing dataset to address the question of what makes nature wild [Software and Data Sets]
B Ekim, TT Stomberg, R Roscher, M Schmitt
IEEE Geoscience and Remote Sensing Magazine 11 (1), 103-114, 2023
132023
Exploring wilderness characteristics using explainable machine learning in satellite imagery
TT Stomberg, T Stone, J Leonhardt, I Weber, R Roscher
arXiv preprint arXiv:2203.00379, 2022
72022
AQ-Bench: a benchmark dataset for machine learning on global air quality metrics, Earth Syst. Sci. Data, 13, 3013–3033
C Betancourt, T Stomberg, R Roscher, MG Schultz, S Stadtler
62021
Leveraging activation maximization and generative adversarial training to recognize and explain patterns in natural areas in satellite imagery
A Emam, TT Stomberg, R Roscher
IEEE Geoscience and Remote Sensing Letters, 2023
42023
Recognizing protected and anthropogenic patterns in landscapes using interpretable machine learning and satellite imagery
TT Stomberg, J Leonhardt, I Weber, R Roscher
Frontiers in Artificial Intelligence 6, 1278118, 2023
32023
Global fine resolution mapping of ozone metrics through explainable machine learning
C Betancourt, S Stadtler, T Stomberg, AK Edrich, A Patnala, R Roscher, ...
EGU General Assembly, 2021
12021
Investigating the contribution of image time series observations to cauliflower harvest-readiness prediction
J Kierdorf, TT Stomberg, L Drees, U Rascher, R Roscher
Frontiers in Artificial Intelligence 7, 1416323, 2024
2024
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