lambdavi/SatDrive-SegFL

MLDL '23 Project: Federated Learning and Semantic Segmentation for Autonomous Driving and Satellite Images Segmentation

45
/ 100
Emerging

This project helps machine learning researchers explore and compare different methods for image segmentation tasks, particularly in autonomous driving and satellite imagery analysis. It takes raw image datasets (like those from autonomous vehicles or aerial photos) and outputs segmented images, highlighting different objects or regions. This is useful for researchers and engineers developing computer vision models for these specific domains.

Use this if you are a machine learning researcher or engineer experimenting with federated learning and semantic segmentation techniques for autonomous driving or satellite image analysis.

Not ideal if you are a practitioner looking for an out-of-the-box solution to directly apply image segmentation without delving into model architecture or training specifics.

autonomous-driving satellite-imagery image-segmentation machine-learning-research computer-vision-engineering
No Package No Dependents
Maintenance 10 / 25
Adoption 4 / 25
Maturity 16 / 25
Community 15 / 25

How are scores calculated?

Stars

7

Forks

4

Language

Python

License

GPL-3.0

Last pushed

Jan 21, 2026

Commits (30d)

0

Get this data via API

curl "https://pt-edge.onrender.com/api/v1/quality/transformers/lambdavi/SatDrive-SegFL"

Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.