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Optimizing crop type mapping for fairness
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DOI：10.1016/j.jag.2025.104672.png)
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Abstract
En 中文
• Introduces methods to address class and parcel size imbalances in crop type mapping. • Evaluates novel and established methods across diverse crop type mapping datasets. • Novel hybrid method outperforms others in resolving class imbalance. • Performance gap between small and large parcels persists despite mitigation strategies.
Keywords:
Crop mapping
Deep learning
Machine learning
Fairness
Artificial intelligence
Class imbalance problem
Journal
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8.6
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5.2K
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2.4W
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Cited Papers
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