Followb1ind1y/Semantic-Segmentation-of-Aerial-Imagery
Semantic Segmentation of Aerial Imagery Project using PyTorch
This project helps classify different elements like buildings, roads, vegetation, and water in aerial photographs. It takes raw aerial images as input and produces detailed maps where each pixel is labeled with what it represents. This tool is useful for urban planners, environmental analysts, or mapping specialists who need to understand land use from satellite or drone imagery.
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Use this if you need to automatically identify and map different land cover types from aerial or satellite images with high precision.
Not ideal if your primary goal is object detection (counting individual items) rather than pixel-level classification of broad land categories.
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Jupyter Notebook
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Apache-2.0
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Last pushed
Mar 20, 2023
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