  The proliferation of remote sensing satellites has resulted in a massive
amount of remote sensing images. However, due to human and material resource
constraints, the vast majority of remote sensing images remain unlabeled. As a
result, it cannot be applied to currently available deep learning methods. To
fully utilize the remaining unlabeled images, we propose a Geographical
Knowledge-driven Representation learning method for remote sensing images
(GeoKR), improving network performance and reduce the demand for annotated
data. The global land cover products and geographical location associated with
each remote sensing image are regarded as geographical knowledge to provide
supervision for representation learning and network pre-training. An efficient
pre-training framework is proposed to eliminate the supervision noises caused
by imaging times and resolutions difference between remote sensing images and
geographical knowledge. A large scale pre-training dataset Levir-KR is proposed
to support network pre-training. It contains 1,431,950 remote sensing images
from Gaofen series satellites with various resolutions. Experimental results
demonstrate that our proposed method outperforms ImageNet pre-training and
self-supervised representation learning methods and significantly reduces the
burden of data annotation on downstream tasks such as scene classification,
semantic segmentation, object detection, and cloud / snow detection. It
demonstrates that our proposed method can be used as a novel paradigm for
pre-training neural networks. Codes will be available on
https://github.com/flyakon/Geographical-Knowledge-driven-Representaion-Learning.
