Deep learning in remote sensing: a review

TL;DR

This review discusses the application of deep learning in remote sensing, highlighting recent advances, challenges, and advocating for domain expertise integration to address large-scale environmental challenges.

Contribution

It provides a comprehensive overview of deep learning techniques in remote sensing and encourages domain experts to leverage these methods for impactful large-scale problems.

Findings

Deep learning has become a powerful tool in remote sensing.

Challenges include data complexity and interpretability.

Resources are provided to simplify adoption of deep learning.

Abstract

Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a 'black-box' solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with. More importantly, we advocate remote sensing scientists to bring their expertise into deep learning, and use it as an implicit general model to tackle unprecedented large-scale influential challenges, such as climate change and urbanization.