B03
Deep learning for satellite-based land use and land cover reconstruction
Prof. Dr. Ribana Roscher
University of Bonn | +49 228 73-60854 |
Summary
The goal of this project is the reconstruction of land use and land cover across Europe from optical satellite data using deep learning. To this end, we will develop spatio-temporal deep neural networks that consider the specific biogeographical characteristics of the regions of interest in order to ensure a high generalization capability across the study region. Furthermore, predictive uncertainties for the derived land use and land cover maps will be determined and the model’s capabilities in the context of multi-task learning will be explored. Finally, the potential of generated data for data augmentation purposes will be evaluated.
Graphical Summary
Contribution to CRC
Land use and land cover maps generated in our project will be reused by other projects in DETECT for a variety of purposes: On the one hand, the A and B clusters will rely on these data to determine relevant regions for their own purposes, such as agricultural or water areas. The C and D clusters, on the other hand, will use the maps as inputs for terrestrial models or for subsequent analysis.
Approach
To train our neural networks we will rely on openly available medium resolution, multispectral imagery from Sentinel-2 and the Landsat satellites. Observations from the LUCAS surveys will be used as reference data. As those data are distributed rather sparsely, special methods for semi-supervised learning will be developed. Bioclimatic auxiliary information shall be incorporated into the workflows to improve generalizability. As for generative models models for data augmentation, we look to adapt state-of-the-art diffusion models from the computer vision community to the domain of multispectral satellite imagery.
Main results in 2022
Creation of an extensive database for satellite data, sparse labels, and auxiliary information. Conceptualization of the general semi-supervised model, as well as approaches for improving its generalizability.
Main results in 2023
Implementation and publication of a framework for incorporating bioclimatic information into classification workflows. Continued implementation of the general semi-supervised model.
Leonhardt, J., Drees, L., Gall, J., Roscher, R. (2024). Leveraging Bioclimatic Context for Supervised and Self-supervised Land Cover Classification. In: Köthe, U., Rother, C. (eds) Pattern Recognition. DAGM GCPR 2023. Lecture Notes in Computer Science, vol 14264. Springer, Cham. https://doi.org/10.1007/978-3-031-54605-1_15
