Preprints
https://doi.org/10.5194/egusphere-2024-498
https://doi.org/10.5194/egusphere-2024-498
19 Mar 2024
 | 19 Mar 2024
Status: this preprint is open for discussion.

Interactive Snow Avalanche Segmentation from Webcam Imagery: results, potential and limitations

Elisabeth Doris Hafner, Theodora Kontogianni, Rodrigo Caye Daudt, Lucien Oberson, Jan Dirk Wegner, Konrad Schindler, and Yves Bühler

Abstract. For many safety-related applications such as hazard mapping or road management, well documented avalanche events are crucial. Nowadays, despite research into different directions, the available data is mostly restricted to isolated locations where it is collected by observers in the field. Webcams are getting more frequent in the Alps and beyond, capturing numerous avalanche prone slopes several times a day. To complement the knowledge about avalanche occurrences, we propose to make use of this webcam imagery for avalanche mapping. For humans, avalanches are relatively easy to identify, but the manual mapping of their outlines is time intensive. Therefore, we propose to support the mapping of avalanches in images with a learned segmentation model. In interactive avalanche segmentation (IAS), a user collaborates with a deep learning model to segment the avalanche outlines, taking advantage of human expert knowledge while keeping the effort low thanks to the model's ability to delineate avalanches. The human corrections to the prediction in the form of positive clicks on the avalanche or negative clicks on the background result in avalanche outlines of good quality with little effort. Relying on IAS, we extract avalanches from the images in a flexible and efficient manner, resulting in a 90 % time saving compared to conventional manual mapping. If mounted in a stable position, the camera can be georeferenced with a mono-photogrammetry tool, allowing for exact geolocation of the avalanche outlines and subsequent use in geographical information systems (GIS). In this way all avalanches mapped in an image can be imported into a designated database, making them available for the relevant safety-related applications. For imagery, we rely on current and archive data from webcams that cover the Dischma valley near Davos, Switzerland and capture an image every 30 minutes during daytime since the winter 2019. Our model and the associated mapping pipeline represent an important step forward towards continuous and precise avalanche documentation, complementing existing databases and thereby providing a better base for safety-critical decisions and planning in avalanche-prone mountain regions.

Elisabeth Doris Hafner, Theodora Kontogianni, Rodrigo Caye Daudt, Lucien Oberson, Jan Dirk Wegner, Konrad Schindler, and Yves Bühler

Status: open (until 11 May 2024)

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Elisabeth Doris Hafner, Theodora Kontogianni, Rodrigo Caye Daudt, Lucien Oberson, Jan Dirk Wegner, Konrad Schindler, and Yves Bühler
Elisabeth Doris Hafner, Theodora Kontogianni, Rodrigo Caye Daudt, Lucien Oberson, Jan Dirk Wegner, Konrad Schindler, and Yves Bühler

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Short summary
For many safety-related applications such as road management, well documented avalanches are important. To enlarge the information, webcams may be used. We propose to support the mapping of avalanches from webcams with a machine learning model that interactively works together with the human. Relying on that model there is a 90 % saving of time compared to the "traditional" mapping. This gives a better base for safety-critical decisions and planning in avalanche-prone mountain regions.