Digital Poster/Demo
In session
Postersession No. 2
,
Sept. 3, 2025,
18:15 –
19:00
Exact timing:
18:15 –
19:00
,
- Helmholtz-Zentrum Hereon
Coastal zones face increasing pressure from both natural forces and human activity, including sea-level rise, erosion, and expanding infrastructure. Understanding how these landscapes evolve over time is essential for informed decision-making in environmental management, urban planning, and climate adaptation.
We present AutoCoast, a web-based platform for long-term coastal monitoring that combines multi-source satellite imagery with machine learning to detect and visualize shoreline changes from 2015 to 2024. Initially developed for the Baltic Sea, the system is being expanded to cover additional regions such as the North Sea.
A key component of the platform is a custom annotation tool that supports rapid image labeling through active learning. This approach reduces manual effort while maintaining high-quality training data. Our curated dataset, based on Sentinel-2 imagery, includes coastal-specific classes such as beaches, marshes, tidal flats, cliffs, and man-made structures. The resulting segmentation model can reliably identify and classify coastal landforms.
To enhance temporal consistency and spatial accuracy, we implement post-processing steps such as tidal normalization and integrate complementary Sentinel-1 radar data for detecting elevation-driven changes and improving resilience to cloud cover.
The user interface supports dynamic visualization and comparison of coastline evolution, enabling exploration of trends in erosion, accretion, and land use change. AutoCoast aims to provide researchers, planners, and policymakers with a practical tool to monitor and respond to coastal dynamics in near real time.
This work illustrates how machine learning, active learning, and satellite earth observation can be effectively combined into a scalable, user-centered system for environmental monitoring.