Automated avalanche mapping with deep learning: from satellite to webcam imagery

Abstract

Humans have been exposed to snow avalanches ever since they inhabited or travelled through mountainous regions. For centuries, ways of managing risk and mitigating damage due to avalanches have been limited. For example, forests prevented the release of avalanches, protecting the villages below. In the past ca. 150 years, avalanche protection measures and risk reduction strategies have become increasingly sophisticated. With a growing population, the rise of alpine tourism, and major transportation corridors running through the mountains, dealing with avalanches has become more important than ever. Operational services such as the avalanche warning service, hazard mapping, or the installation of protection and mitigation measures for endangered zones are important tools for avalanche risk management. The efficiency of these tools depends on knowledge about past avalanche occurrences. In this thesis, novel methods are developed to automatically provide this information over large regions. Specifically, state-of-the-art deep learning technology is used to extract such information from optical satellite and webcam imagery. The avalanches required to train the deep learning models are identified and mapped by human experts in the domain. Therefore, the thesis also focuses on the consistency of the avalanche area identified by various domain experts. In the first part of the thesis, a DeeplabV3+ model is adapted to automatically identify and map avalanches from optical SPOT 6/7 satellite imagery (1.5 m resolution). The model is trained, validated, and tested with more than 24’000 manually annotated avalanche polygons. The data originate from two avalanche periods in January 2018 and January 2019 and cover an area of more than 22’000 km2. In addition, the quality of the model and the reproducibility of avalanches manually annotated by experts are assessed for a small subset of the data. The second part of the thesis investigates in more detail the reproducibility of estimates of avalanche dimensions by human experts in three user studies. The first study analyzes the classification of ten avalanches into five standardized size categories by each of 170 avalanche experts from Europe and North America. The second and the third study examine avalanches manually mapped from oblique photographs (6 avalanches, 10 participants) or from remotely sensed imagery (2.9 km2, 5 participants), respectively. The third part of the thesis leverages interactive avalanche segmentation (IAS) to combine human expert knowledge with deep learning. Here, when mapping avalanches from webcam imagery, the user collaborates with the previously trained model. The use of the model is supposed to make avalanche mapping more accurate and efficient. For this purpose, we adapt a state-of-the-art interactive segmentation model based on HRNet+OCR and train it for avalanche segmentation from webcam imagery. The human user interacts with the model through confirming or corrective feedback. In summary, this thesis makes a substantial contribution to the development of an operational automatic avalanche mapping service. The thesis provides the first automatic avalanche mapping from optical satellite imagery with deep learning, it quantifies, for the first time to this extent, the reproducibility of human avalanche estimates, and it presents a first interactive approach for the mapping of avalanches from webcam imagery. Thereby, this thesis contributes to a more efficient use of data and better provision of information on past avalanche occurrences, and thus it benefits decision making in safety-relevant applications.