The project addresses the problem of efficiently conducting operational vegetation mapping using satellite data, which is hindered by two main challenges: the high costs and logistical difficulties of acquiring in-situ reference data, and the increasing computational and storage demands associated with processing large volumes of earth observation data. To overcome these obstacles, the project aims to develop a satellite-based processing chain that integrates Unmanned Aerial Vehicles (UAVs) and Deep Learning Algorithms for automated reference data acquisition. These UAV-derived vegetation maps will serve as the foundation for large-scale vegetation mapping based on satellite data.
Associated researchers | Teja Kattenborn, Felix Schiefer |
Collaborators | Sebastian Schmidtlein, LUP – Luftbild Umwelt Planung |
Duration | 2020-2024 |
Funding | DLR / BMWi |