Schönbrodt, S.; Dohmen, H.; Pommer, P.; Schneider, S.; Berberich, N.; KI macht Schule Team (2024): Bildung mit und uber KI in der Schule: Umsetzung einer partizipativen Unterrichtsplattform. Proceedings of DELFI Workshops 2024. Gesellschaft für Informatik e.V. doi: 10.18420/delfi2024-ws-21

Rau, K., Eggensperger, K., Schneider, F., Hennig, P. and Scholten, T. (2024). How can we quantify, explain, and apply the uncertainty of complex soil maps predicted with neural networks? Science of The Total Environment, 944, 173720. p. doi: 10.1016/j.scitotenv.2024.173720

Rau, K.., Eggensperger, K., Schneider, F., Hennig, P., & Scholten, T.: How can we quantify, explain and apply the uncertainty of complex soil maps predicted with neural networks? EGU General Assembly 2024, Vienna, Austria. [talk]

Rau, K., Eggensperger, K., Schneider, F., Hennig, P., & Scholten, T.: How can we quantify, explain and apply the uncertainty of complex soil maps predicted with neural networks? Centennial of the IUSS 2024, Florence, Italy. [talk]

Rau, K., Eggensperger, K., Schneider, F., Hennig, P., & Scholten, T.: How can we quantify, explain and apply the uncertainty of complex soil maps predicted with neural networks? AHM of the German AI Competence Centers, Berlin, Germany. [poster]

Rau, K., Eggensperger, K., Schneider, F., Hennig, P., & Scholten, T.: How can we quantify, explain and apply the uncertainty of complex soil maps predicted with neural networks?  Jahrestagung der Deutschen Bodenkundlichen Gesellschaft 2023, Halle, Germany. [talk]

Rau, K., Gläßle, T., Hennig, P., & Scholten, T.: Spatial prediction of soil type maps with Neural Networks including quantification of model uncertainty. 22nd World Congress of Soil Science 2022, Glasgow, United Kingdom. [poster]

Rau, K., Gläßle, T., Hennig, P., & Scholten, T.: Spatial prediction of soil type maps with Neural Networks including quantification of model uncertainty. ELLIS Doctoral Symposium 2022, Alicante, Spain. [poster]

Rau, K., Gläßle, T., Hennig, P., & Scholten, T.: Spatial prediction of soil type maps with Neural Networks including quantification of model uncertainty. Jahrestagung der Deutschen Bodenkundlichen Gesellschaft 2022, Trier, Germany. [talk]

Rau, K., Gläßle, T., Hennig, P., & Scholten, T.: Spatial prediction of soil type maps with Neural Networks including quantification of model uncertainty. EGU General Assembly 2022, Vienna, Austria. [talk]

Gläßle, T., Rau, K., Scholten, T., & Hennig, P.: Hierarchical Soil Classification using Gaussian Processes. EGU General Assembly 2022, Vienna, Austria. [talk]

Rau, K., Gläßle, T., Rentschler, T., Hennig, P., & Scholten, T.:  Spatial prediction of soil thickness with Gaussian Process Regression using pedological knowledge described by partial differential equations. EGU General Assembly 2021, Vienna, Austria. [vPICO]

Gläßle, T., Rau, K., Schmidt, K., Scholten, T., & Hennig, P.: Topographic Kernels for Gaussian Process Regression in Digital Soil Mapping. EGU General Assembly 2021, Vienna, Austria. [vPICO]