| 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 | 20 | 2022 |
| 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 | 19* | 2021 |
| 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 | 16 | 2021 |
| 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 | 6 | 2022 |
| 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 | 5 | 2023 |
| 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 | 2 | 2023 |
| 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 | 1 | 2023 |
| 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 | 1 | 2021 |
Ribana RoscherResearch Center Jülich and University of Bonn在 fz-juelich.de 的电子邮件经过验证
