satellite-image-deep-learning / techniques
Techniques for deep learning with satellite & aerial imagery
Why this job: topics: dataset, datasets

What it does
Deep learning has revolutionized the analysis and interpretation of satellite and aerial imagery, addressing unique challenges such as vast image sizes and a wide array of object classes. This repository provides an exhaustive overview of deep learning techniques specifically tailored for satellite and aerial image processing. It covers a range of architectures, models, and algorithms suited for key tasks like classification, segmentation, and object detection.
Get started
No single install command. Read the steps in the README (opens in new tab)
- Maintenance
- Active
- Last commit 3 days ago
- Stars
- 10,301
- +6 this week
- Licence
- Apache-2.0
- Allows business use
- Downloads for
- None listed
- No release files
- Latest version
- v1.3
- Released 16 months ago
- Built with
- Not stated
Is it looked after?
- Contributors
- 3
- Last 90 days
- Issues
- 100% closed
- 0 still open
- Pull requests
- 52 merged
- 0 waiting
- Releases
- 23
- 0 in the last year
- No contributing guide
- No security policy
- No code of conduct
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More: topics and project record
Topics
- deep-learning
- deep-neural-networks
- satellite-imagery
- pytorch
- python
- machine-learning
- sentinel
- satellite-images
- dataset
- remote-sensing
- datasets
- convolutional-neural-networks
Project record
- JobFine-tuning
- StatusListed
- Commits1,518
- Forks1,646
- Watching278
- Stars in 28 days+28
- CreatedApr 16, 2018
- First seen by usNov 7, 2026
- CheckedNov 10, 2026
