ayushdabra/drone-images-semantic-segmentation
Multi-class semantic segmentation performed on "Semantic Drone Dataset."
This project helps operations managers, urban planners, or environmental scientists to automatically categorize elements within drone imagery. You provide aerial photos of urban environments, and it outputs an image where each pixel is colored according to what it represents (e.g., paved area, grass, roof, water, person). This tool helps in efficiently analyzing land use and urban features from a bird's-eye view.
104 stars. No commits in the last 6 months.
Use this if you need to precisely identify and map different objects or land types from high-resolution drone photos of urban areas.
Not ideal if your primary interest is in detecting individual objects rather than classifying every single pixel by its category, or if your images are not from urban drone datasets.
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104
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20
Language
Jupyter Notebook
License
GPL-3.0
Category
Last pushed
Aug 08, 2021
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