  Many current deep learning approaches make extensive use of backbone networks
pre-trained on large datasets like ImageNet, which are then fine-tuned to
perform a certain task. In remote sensing, the lack of comparable large
annotated datasets and the wide diversity of sensing platforms impedes similar
developments. In order to contribute towards the availability of pre-trained
backbone networks in remote sensing, we devise a self-supervised approach for
pre-training deep neural networks. By exploiting the correspondence between
geo-tagged audio recordings and remote sensing imagery, this is done in a
completely label-free manner, eliminating the need for laborious manual
annotation. For this purpose, we introduce the SoundingEarth dataset, which
consists of co-located aerial imagery and audio samples all around the world.
Using this dataset, we then pre-train ResNet models to map samples from both
modalities into a common embedding space, which encourages the models to
understand key properties of a scene that influence both visual and auditory
appearance. To validate the usefulness of the proposed approach, we evaluate
the transfer learning performance of pre-trained weights obtained against
weights obtained through other means. By fine-tuning the models on a number of
commonly used remote sensing datasets, we show that our approach outperforms
existing pre-training strategies for remote sensing imagery. The dataset, code
and pre-trained model weights will be available at
https://github.com/khdlr/SoundingEarth.
