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satellite-image-deep-learning / techniques

Techniques for deep learning with satellite & aerial imagery

Why this job: topics: dataset, datasets

Picture from the satellite-image-deep-learning/techniques README

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 2 days ago
Stars
10,301
+7 this week
Licence
Apache-2.0
Allows business use
Downloads for
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No release files
Latest version
v1.3
Released 16 months ago
Built with
Not stated

Is it looked after?

Contributors
Not stated
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+31
  • CreatedApr 16, 2018
  • First seen by usNov 7, 2026
  • CheckedNov 8, 2026

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