Deep learning models based on diffusion processes have shown great potential in a range of generative tasks, such as image generation. For remote sensing applications, generative models are not that common. The question that we tried to answer is whether diffusion processes can be used to efficiently pretrain models for discriminative tasks?

Multimodal datasets for remote sensing are oftentimes limited to two data modalities, such as multispectral and SAR polarization data. In order to experiment with a much wider range of data modalities, we extended the well-known BigEarthNet dataset to includes a wide range of data modalities.

Road traffic noise is a global issue that can lead to severe health effects. Despite the ubiquity of traffic noise, its quantification or estimation is complicated and detailed road traffic maps are only available for select countries or areas. We investigate whether it is possible to train a segmentation model to esimate road traffic noise from satellite imagery.