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Where are the ravines? A Case Study of Gully Landscapes in Norway Using Deep Learning
  • Alexandra Jarna Ganerød,
  • Mikis van Boeckel,
  • Inger-Lise Solberg
Alexandra Jarna Ganerød
Department of Geography, Norwegian University of Science and Technology, 7049 Trondheim, Norway, Geological Survey of Norway (NGU), 7040 Trondheim, Norway

Corresponding Author:[email protected]

Author Profile
Mikis van Boeckel
Geological Survey of Norway (NGU), 7040 Trondheim, Norway
Inger-Lise Solberg
Geological Survey of Norway (NGU), 7040 Trondheim, Norway


Gullies and ravines are common landforms in raised marine fine-grained deposits in Norway. Gullies in marine clay are significant landforms indicative of soil erosion, natural hazards and are of high conservation value. As a result of the substantial impact of human intervention over the past century, marine clay gullies are now red-listed. To monitor the condition of these landforms we need to improve our understanding of their spatial extent, complexity, and morphology. We explore the applicability of automated approaches that uses a methodology of combining deep learning (DL), fully convolutional neural networks (FCNN), and a U-Net model with ArcPy libraries and ground truth data to derive a high-resolution map of gullies in raised marine fine-grained deposits. Predictors used comprise solely terrain derivatives to broaden the usage of the pre-trained model to other regions. Our best model achieved a precision score of 0.82 and a recall of 0.75. We find that our pre-trained model can successfully predict gullies in blind-test areas. The model performs better in regions with similar geological settings, scoring a length-weighted overlap of >72% with reference datasets. We also find that the model's applicability increases when we post-process the predictions by eliminating noise, especially by using the predictions derived from ensembled models. We, therefore, conclude that the pre-trained models can effectively be used to supplement the geomorphological mapping of marine clay gullies in Norway. The outcome of this research contributes towards mapping the spatial extent and condition of red-listed landforms in Norway, as well as the development of monitoring systems for future landscape change.  
Keywords: gullies, ravines, landforms, marine clay, deep learning, U-net