#StandForSudan historic data for graphs with daily updates
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Updated
Apr 21, 2020 - Python
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sudan
Here are 2 public repositories matching this topic...
Multi-class crop classification in Elgabel Region, Sudan using Sentinel-2 imagery with scikit-learn (MLP, XGBoost, Random Forest) and PyTorch deep learning (CNN1D, Hybrid CNN+MLP, Transformer). Achieves 100% accuracy with FocalLoss, SMOTE, and class weighting.
machine-learning deep-learning random-forest geospatial pytorch remote-sensing xgboost google-earth-engine sentinel-2 sudan crop-classification
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Updated
Jan 29, 2026 - Python
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