microsoft/data-centric-satellite-segmentation
Contains implementations of data-centric approaches for improving semantic segmentation on satellite imagery.
This project helps urban planners, environmental analysts, or mapping professionals improve the accuracy of automatically identifying features like buildings or roads in satellite images. It takes raw satellite imagery and associated ground truth maps as input, and outputs refined training data selections and segmentation models that are more precise at classifying objects in the images. This is for professionals who analyze satellite data and need highly accurate land cover maps.
No commits in the last 6 months.
Use this if you need to create more accurate land cover maps or detect specific features from satellite imagery, especially when working with limited labeled data or trying to optimize your training process.
Not ideal if you are looking for an out-of-the-box solution without any technical setup, or if your primary interest is in general object detection rather than precise pixel-level segmentation of satellite images.
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56
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3
Language
Python
License
MIT
Category
Last pushed
Apr 10, 2025
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