Project description
The AI4ENV project focuses on advancing 4D (3D + time) data analysis for monitoring natural surface dynamics, including landslides, coastal erosion, glacier movement, and riverbed morphology changes. As climate change accelerates these processes, their societal and economic impacts—such as health risks and damage to infrastructure—are expected to grow.
4D datasets serve as digital twins of critical environments affected by climate change. Through repeated acquisitions of 3D topographic data, they provide precise information about surface geometry and physical properties, essential for geoscientific research and large-scale environmental monitoring. However, improved data processing methods are required to fully harness the potential of these datasets and generate new insights.
The AI4ENV project addresses these challenges by applying advanced artificial intelligence techniques to analyze spatiotemporal digital twins of fragile (pre-)alpine environments. It focuses on overcoming obstacles such as aligning multi-source 4D datasets with varying coverage and resolution. Additionally, the project will enhance the use of multi-modal remote sensing data to improve our understanding and prediction of surface processes in vulnerable regions.
Objectives
The project addresses two main research questions:
- How can multi-source topographic data, particularly 3D point clouds, be automatically integrated into 4D datasets to provide detailed and relevant information about natural surface dynamics?
- How can 4D datasets be effectively utilized with advanced AI methods to gain insights into change dynamics in natural environments and progress towards actionable outcomes?