Self-supervised Audiovisual Representation Learning for Remote Sensing Data

Understand

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.