nive927/Dubai-Satellite-Imagery-Multiclass-Segmentation
Simulation and performance analysis of 3 benchmark models (Standard U-Net, U-Net with Resnet backbone & U-Net with DeepLabV3+ backbone) for Multiclass Semantic Segmentation of Satellite Images.
This project helps urban planners, environmental analysts, or government agencies automatically identify and map features like buildings, roads, water, and vegetation from satellite images. It takes raw satellite imagery as input and produces a segmented map where each pixel is classified into one of six categories. Urban developers or environmental scientists can use these detailed maps for analysis or planning.
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Use this if you need to precisely classify different land cover types in aerial or satellite imagery for urban development, environmental monitoring, or geographical analysis.
Not ideal if you are looking to analyze very high-resolution drone imagery or require classification of very specific, nuanced features beyond standard land cover types.
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Jan 21, 2022
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