KostasStefanidis/Semantic-Segmentation

Semantic Segmentation for Urban Scene understanding - Cityscapes dataset

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Emerging

This tool helps urban planners and autonomous vehicle engineers analyze street-level images by automatically identifying and labeling objects like roads, buildings, and pedestrians at a pixel level. You provide it with images from the Cityscapes dataset, and it outputs segmented images where each pixel is colored according to its object class. It's designed for professionals who need precise, automated classification of urban visual data.

No commits in the last 6 months.

Use this if you need to precisely segment objects within urban street scenes for tasks like autonomous driving development, urban planning, or environmental monitoring.

Not ideal if your image segmentation needs are outside of urban street scenes or if you require real-time inference on edge devices with limited computational power.

urban-planning autonomous-vehicles geospatial-analysis environmental-monitoring computer-vision
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 5 / 25
Maturity 16 / 25
Community 12 / 25

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Stars

11

Forks

2

Language

Python

License

GPL-3.0

Last pushed

Aug 22, 2023

Commits (30d)

0

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