• 2023

    • Acquistapace C., S. Schnitt, N. Risse, D. Ori, S. Krause, D. Chatterjee, D. Lange, F. Späth, I. Mc Coy, T. Seelig, 2023: Characterization of precipitation life cycle in the trades across different regimes of shallow convection. Quarterly Journal of the Royal Meteorological Societ
    • Chatterjee, D., C. Acquistapace, H. Deneke, S. Crewell, 2023: Understanding cloud systems structure and organization using a machine’s self-learning approach. Artif. Intell. Earth Syst, DOI: 10.1175/AIES-D-22-0096.1
    • Chatterjee, D., S. Schnitt, P. Bigalke, C. Acquistapace, S. Crewell, 2023: Capturing the diversity of mesoscale trade wind cumuli using complementary approaches from self-supervised deep learning. Geophysical research letters, DOI:10.22541/essoar.170000384.49382400/v1
    • Edrich, A., Yildiz, A., Roscher, R., Graf, F., Bast, A., Kowalski, J., 2023. A modular framework for FAIR shallow landslide susceptibility mapping based on machine learning. Natural Hazards DOI:10.21203/rs.3.rs-3254996/v1
    • Emam, A., Stomberg T., Roscher, R., 2023. Leveraging Activation Maximization and Generative Adversarial Training to Recognize and Explain Patterns in Natural Areas in Satellite Imagery. Geoscience and Remote Sensing Letters                   DOI: 10.1109/LGRS.2023.3335473
    • Ekim, B., Stomberg, T.T., Roscher, R., and Schmitt M., 2023. MapInWild: A remote sensing dataset to address the question of what makes nature wild [Software and Data Sets]. IEEE Geoscience and Remote Sensing Magazine 11, 103-114.                DOI: 10.1109/MGRS.2022.3226525.
    • Stomberg, T.T., Leonhardt J., Weber I., and Roscher R., 2023. Recognizing Patterns of Protected and Anthropogenic Areas in Remote Sensing Imagery Using Interpretable Machine Learning. Frontiers in AI. DOI: 10.3389/frai.2023.1278118
    • P. Yousefi K., Kollet S. (2023) Deep learning of model- and reanalysis-based precipitation and pressure mismatches over Europe. Front. Water 5:1178114.DOI: 10.3389/frwa.2023.1178114
    • Patnala, A., Stadtler, S., Schultz, M. G., & Gall, J. (2023). Generating views using atmospheric correction for
      contrastive self-supervised learning of multispectral images. IEEE Geoscience and Remote Sensing Letters, 20, 1-5.         DOI: 10.1109/LGRS.2023.3274493
    • Patnala, A., Stadtler, S., Schultz, M. G., & Gall, J. (2023).

      Multi-modal contrastive learning for crop classification using Sentinel2 and Planetscope.                                                  preprint: DOI: 10.36227/techrxiv.24032043.v1

  • 2022

    • Betancourt, C., Stomberg, T. T., Edrich, A.-K., Patnala, A., Schultz, M. G., Roscher, R., Kowalski, J., & Stadtler, S. (2022). Global, high-resolution mapping of tropospheric ozone – explainable machine learning and impact of uncertainties. Geoscientific Model Development, 15(11), 4331–4354. DOI: 10.5194/gmd-15-4331-2022
    • Stomberg, T.T., Stone, T., Leonhardt, J., Weber, I., and Roscher, R., 2022. Exploring Wilderness Characteristics Using Explainable Machine Learning in Satellite Imagery (Preprint).; arxiv. DOI: 10.48550/arXiv.2203.00379.
    • P. Yousefi, K., Kollet, S., 2022. Data for: Deep Learning of Model- and Reanalysis- Based Precipitation and Pressure Mismatches over Europe. DOI:10.5281/zenodo.6882196
  • 2021

    • Stomberg, T., Weber, I., Schmitt, M., and Roscher, R., 2021. JUNGLE-NET: USING EXPLAINABLE MACHINE LEARNING TO GAIN NEW INSIGHTS INTO THE APPEARANCE OF WILDERNESS IN SATELLITE IMAGERY. ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci. V-3-2021, 317–324. DOI: 10.5194/isprs-annals-V-3-2021-317-2021.