nasaharvest/crop-maml
Learning to predict crop type from heterogeneous sparse labels using meta-learning
This project helps agricultural researchers and food security analysts create accurate crop type maps using satellite imagery. It takes raw Earth observation data and limited, varied crop type labels as input, then produces detailed maps showing where specific crops like coffee or common beans are grown. This is useful for anyone needing to understand agricultural land use and production, especially in regions with scarce ground truth data.
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Use this if you need to create precise crop type maps for agricultural monitoring or food security analysis, especially when you have only a few examples of labeled crop fields.
Not ideal if you need a plug-and-play solution for general land cover mapping that isn't specifically focused on crop types, or if you don't have access to satellite imagery and some form of crop labels.
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Language
Python
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Last pushed
Jun 17, 2021
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