satellite-image-deep-learning/techniques resource

Health v2 · maintenance only

Adoption not part of the score

10242 stars · 1643 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A curated reference repository cataloguing deep learning techniques, models, and datasets for satellite and aerial imagery analysis. It organizes resources by task such as classification, segmentation, object detection, change detection, and crop monitoring.

Use cases

• find deep learning models for satellite image segmentation
• learn techniques for object detection in aerial imagery
• discover datasets for remote sensing research
• find approaches for cloud detection and removal in satellite images
• explore crop classification and yield forecasting methods
• find self-supervised learning resources for earth observation
• learn about SAR image analysis with deep learning

When to choose

• you are starting a remote sensing or earth observation ML project and need an overview of available techniques
• you want curated links to models, papers, and datasets for satellite imagery tasks
• you need to compare approaches for tasks like change detection or image classification on aerial data

When to avoid

• you need runnable production code rather than a curated list of links
• you are working with non-imagery data or general computer vision unrelated to aerial/satellite imagery

Facets

learning-resource · maturity active

deep-learning machine-learning image-processing computer-vision data-science deep-learning machine-learning computer-vision image-processing python cross-platform satellite-imagery remote-sensing awesome-list earth-observation segmentation object-detection sentinel aerial-imagery pytorch geospatial

Member repositories

For agents

markdown · JSON · MCP: product_card(name="satellite-image-deep-learning/techniques")