Deep learning for satellite-based land use and land cover reconstruction (B03)

• Subject Area: Geodesy, Photogrammetry, Remote Sensing, Geoinformatics, Cartography ; Image and Language Processing, Computer Graphics and Visualisation, Human Computer Interaction, Ubiquitous and Wearable Computing
• Term: since 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 450058266

The goal of this project is the determination of land use and land cover from optical satellite data for specific points in time (as a snapshot) or for longer periods of time (e.g. one season). For this purpose, deep neural networks will be developed that take into account the specific biogeographical characteristics of the regions of interest in order to ensure a high generalization capability. Furthermore, spatiotemporal data gaps will be closed to improve the data basis for the developed methods and data and model uncertainties for the derived land use and land cover maps will be determined.

• DFG Programme: Collaborative Research Centres
• Subproject of: SFB 1502: Regional Climate Change: Disentangling the Role of Land Use and Water Management
• Applicant Institution: Rheinische Friedrich-Wilhelms-Universität Bonn
• Project Head: Professorin Dr.-Ing. Ribana Roscher