AronBakes/Semantic-Segmentation-of-Aerial-Imagery
Semantic Segmentation of Aerial Imagery - Multi-modal deep learning models (U-net, SegFormer, logistic regression + CRF) trained on RGB and elevation data for aerial scene understanding.
This project helps urban planners, environmental analysts, or infrastructure managers automatically identify objects like buildings, vegetation, and roads in drone or aerial images. You input raw RGB and elevation data from aerial surveys, and it outputs detailed maps where each pixel is classified into categories such as 'building', 'vegetation', or 'water'. This allows for faster and more consistent analysis of large areas.
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Use this if you need to precisely classify features in aerial or drone imagery for land-use mapping, environmental monitoring, or urban development planning, especially when elevation data is available.
Not ideal if you only need to detect the presence of objects without pixel-level classification, or if you don't have access to elevation data alongside your RGB imagery.
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MIT
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Last pushed
Aug 06, 2025
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