Open MIND· 2017· Review
Deep learning in remote sensing: A comprehensive review and list of resources
- 2,813citations
- 2017year
Short summary
This review analyzes the challenges and recent advances of using deep learning for remote sensing data analysis, advocating for its application to large-scale challenges like climate change and urbanization.
AI-generated from the title and abstract; the full text is not read.
Key points
- Deep learning offers powerful capabilities for remote sensing data analysis.
- Challenges exist in applying deep learning, including its 'black-box' nature.
- The paper reviews recent advances and provides resources for easier adoption.
- Integration of domain expertise with deep learning is encouraged for global challenges.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source. Open MIND, 2017
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Field: Media Technology
Media TechnologyEngineering