Abstract
Central to the looming paradigm shift toward data-intensive science, machine-learning techniques are becoming increasingly important. In particular, deep learning has proven to be both a major breakthrough and an extremely powerful tool in many fields. Shall we embrace deep learning as the key to everything? Or should we resist a black-box solution? These are controversial issues within the remote-sensing community. In this article, we analyze the challenges of using deep learning for remote-sensing data analysis, review recent advances, and provide resources we hope will make deep learning in remote sensing seem ridiculously simple. More importantly, we encourage 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.
| Original language | English |
|---|---|
| Article number | 8113128 |
| Pages (from-to) | 8-36 |
| Number of pages | 29 |
| Journal | IEEE Geoscience and Remote Sensing Magazine |
| Volume | 5 |
| Issue number | 4 |
| DOIs | |
| State | Published - Dec 2017 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
-
SDG 13 Climate Action
Fingerprint
Dive into the research topics of 'Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver