Orion-AI-Lab/EfficientBigEarthNet
Code and models for efficient training on the BigEarthNet dataset for Land Use Land Cover classification
This project offers a collection of pre-trained models for classifying land use and land cover (LULC) from satellite images, specifically from Copernicus Sentinel-2. You input raw Sentinel-2 multispectral imagery, and the models output classifications for various land cover types like forests, urban areas, or water. This is designed for remote sensing specialists, environmental analysts, urban planners, and researchers who need accurate and efficient land classification.
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Use this if you need to rapidly classify land use and land cover from Sentinel-2 satellite imagery and want to leverage pre-trained, high-performing models to save development time and computational resources.
Not ideal if your primary data source is not Sentinel-2 imagery or if you need to classify objects with very fine-grained distinctions not typically covered by broad LULC categories.
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74
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Language
Python
License
MIT
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Last pushed
Dec 07, 2022
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