Nora Gourmelon

Lehrstuhl für Informatik 5 (Mustererkennung) Lehrstuhl für Informatik 14 (Bild- und Sprachverarbeitung) (LME)

PASAL: Progress- and sparsity-aware loss balancing for heterogeneous dataset fusion (2025) Gorges T, Scholz T, Saloman S, Zinnen M, Hoffmann J, Gourmelon N, Maier A, et al. Journal article SSL4SAR: Self-Supervised Learning for Glacier Calving Front Extraction from SAR Imagery (2025) Gourmelon N, Dreier MN, Mayr M, Seehaus T, Pyles DR, Braun M, Maier A, Christlein V Journal article Contextual HookFormer for Glacier Calving Front Segmentation (2024) Wu F, Gourmelon N, Seehaus T, Zhang J, Braun M, Maier A, Christlein V Journal article, Original article A Benchmark Dataset for Radio Echo Sounding Data (2024) Dreier MN, Gourmelon N, Koch M, Seehaus T, Braun M, Maier A, Christlein V Conference contribution, Abstract of a poster Out-of-the-box Calving Front Detection Method Using Deep Learning (2023) Herrmann O, Gourmelon N, Seehaus T, Maier A, Fürst J, Braun M, Christlein V Journal article, Original article CaFFe - a Benchmark Dataset for Glacier Calving Front Extraction from Synthetic Aperture Radar Imagery (2023) Gourmelon N, Seehaus T, Klink J, Braun M, Maier A, Christlein V Conference contribution, Conference Contribution Conditional Random Fields for Improving Deep Learning-based Glacier Calving Front Delineations (2023) Gourmelon N, Klink J, Seehaus T, Braun M, Maier A, Christlein V Conference contribution, Conference Contribution AMD-HookNet for Glacier Front Segmentation (2023) Wu F, Gourmelon N, Seehaus T, Zhang J, Braun M, Maier A, Christlein V Journal article, Original article Comparability of Deep Learning Techniques for Calving Front Segmentation in SAR Imagery (2023) Gourmelon N, Seehaus T, Braun M, Maier A, Christlein V Conference contribution, Abstract of lecture Calving fronts and where to find them: a benchmark dataset and methodology for automatic glacier calving front extraction from synthetic aperture radar imagery (2022) Gourmelon N, Seehaus T, Braun M, Maier A, Christlein V Journal article, Original article