theobdt/aerial_pc_classification
Segmentation of urban aerial point clouds with Deep Learning in Pytorch.
This project helps urban planners, GIS analysts, and cartographers automatically classify urban aerial point cloud data. It takes raw 3D point cloud scans of cities and identifies different features like buildings, vegetation, roads, and ground. The output is a categorized 3D model of the urban environment, making it easier to analyze city landscapes.
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Use this if you need to automatically segment and label large urban aerial point cloud datasets into distinct categories like buildings, trees, and ground.
Not ideal if you are working with non-urban environments, require real-time processing, or need to classify objects not present in typical urban scenes.
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Python
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Last pushed
Apr 13, 2020
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