GLACIA: A satellite AI-powered tool to monitor glacier-fed lake dynamics for climatesmart water use
GLACIA (Glacier-Lake Analytics through Copernicus and Intelligent Algorithms) is a data-driven platform using Copernicus Sentinel imagery and machine learning to monitor glacier-fed lake dynamics in the Swiss Alps. By detecting changes in lake area, glacier melt timing, and inflow patterns, GLACIA supports climate risk assessment and water availability forecasting. The system enables smarter, adaptive water resource planning in Switzerland and other glacier-dependent regions.
What issue does your project address, and why do you want to tackle it?
Climate change is accelerating glacier melt in the Alps, reshaping the dynamics of glacier-fed lakes. This impacts inflow timing, flood risk, and long-term water availability. Current monitoring is sparse or reactive. GLACIA uses satellite data-driven and intelligent algorithms to track lake-glacier interactions at both regional and local scales - supporting climate adaptation and smarter, forward-looking water planning.
What is your project about and how does it make Switzerland water-wise?
GLACIA uses Copernicus SenOnel-1 and -2 data combined with machine learning models to monitor glacier-fed lakes and associated meltwater dynamics. Built on cloud-based big data plaRorms like Google Earth Engine, the system enables highresolution (10m) tracking of lake surface changes, snowmelt Oming, and inflow pattrns. It provides Omely insights for climate adaptation and smarter water planning across Swiss authorithies, research, and environmental management sectors.
How do you measure your project's success?
Success is measured by delivering a scalable, cloud-based tool to assess and track glacier-fed lake systems. Key outcomes include the tool’s ability to detect changes and forecast trends, its adoption by end users (e.g. planners, researchers), open data accessibility, and feedback from stakeholders. The platform's usability, geographic scalability, and its role in climate-informed decision-making will define its long-term impact.
Who are the people behind the project and what is your secret to a great collaboration?
For now just me, I’m Ma=eo Marzo, an Aerospace Engineer with experience in the European space industry and ESA (JUICE mission to Jupiter: Monte Carlo radiation simulations for a gravity-measuring transponder and a radar probing subsurface water on Europa), CERN (parOcle transport simulaOons across LHC and SPS, 2 publications), and data/project roles in Swiss banking. I’m now launching a startup in remote sensing. I blend science, space tech, and AI, and collaborate openly to turn ideas into tools for Earth and climate.
