IEEE Geoscience and Remote Sensing Magazine· 2017Q1· Review
Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources
- 3,043citations
- Q1SCImago
- 2017year
Short summary
This review analyzes the challenges and recent advances of applying deep learning to remote sensing data, aiming to simplify its adoption for scientists.
AI-generated from the title and abstract; the full text is not read.
Key points
- Deep learning offers a breakthrough for remote sensing data analysis.
- The paper reviews challenges and recent advances in this application.
- Resources are provided to simplify deep learning adoption for remote sensing scientists.
- The goal is to enable tackling large-scale challenges like climate change and urbanization.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source. IEEE Geoscience and Remote Sensing Magazine, 2017 · DOI ↗
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