Improved spatial and temporal monitoring of vegetation through the integration of multi-modal and meteorological data.

The country wide continuous monitoring of vegetation with high spatial and temporal resolution is essential for various aspects related to climate change and the green transition. However, despite the availability of various remote sensing sensors and methods, existing approaches individually do not provide such complex monitoring capabilities. Data fusion of the individual sensors, especially in combination with deep learning, has recently gained attention and has already achieved promising results. Within imvEOm we interpret the individual timestamps of the various sensors as frames of a multi modal "video" showing the spatial and temporal evolution of vegetation. Following this assumption, we will adapt approaches specifically designed for extracting spatio-temporal features from sequential data to the monitoring of the vegetation across Austria.