April 8, 2026 – Inspired by an Instagram story posted by ETH, we – three students from different fields, at different points in their studies – decided to participate in the EAGLE Hackathon at SLF. Each of us wanted to gain insight into a thus far foreign field of research.

Mass movement pose a risk for infrastructure and people living in alpine regions. With climate change, these turn even more unpredictable. Therefore, it is important to have working monitoring systems to prevent infrastructure damages and evacuate areas where mass movements might occur. The EAGLE project at SLF focuses on detecting mass movements using radar data from satellites.

The three of us – Bhawana, Lilian, and Sujani – worked on current challenges that the EAGLE project at SLF faced regarding their Machine Learning (ML) based analysis of mass movements. Trying a new arrangement, we worked five of the six weeks remotely, with occasional checkpoints, after which we spent a week in Davos for the finalisation of our project.

Coming from different educational backgrounds, we combined our strengths to work on the main issues. First, the new ML model, insarpy, needed to be integrated into QGIS. After that, the model was to be adapted to accept user inputs to modify the predictions. Having completed these tasks, we decided to additionally improve the user experience when working with the QGIS plugin. Lastly, we attempted the final task of containerisation using Docker.

As the project culminated with the stay in Davos, we wanted to experience as much as we could in the given time. Since our time there fell just before Easter, we got to experience the typical Easter-time snowfall. Making use of this, some of us went on a walk in the snow, enjoying the beautiful scenery. Others tried out cross-country skiing. On the not-so-nice days, we made use of our cozy apartment, for instance by watching movies or baking.

“Coming into this project, I had no prior experience in this domain. But getting to apply data science to real data, with all its messiness and errors, taught me so much more than any classroom could. The learning environment was incredible, and for me it was a chance to step into a new field and see how data science actually works in practice” – Bhawana Bhawana

“Coming from a non-coding background, this internship was a great opportunity for me to get a glimpse into the everyday life of programming – experiencing the struggles and rewards that researchers and data scientists have on a daily basis.” – Lilian Obrist

“This project was a nice introduction into working on real life issues in an interdisciplinary team.” – Sujani Mugunthan

All in all, the project gave us the opportunity to apply our skills on a new and interesting topic, and to learn new things that fall beyond our fields of study. We learned, that sometimes, not everything works at first try and about the power of brainstorming. In addition, we were able to experience Davos in its fullest “winter” beauty.