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    Satellite Image Price Prediction Based on Machine Learning by Linhan Yang, Zugang Chen, Guoqing Li

    Published 2025-06-01
    “…For optical imagery, the Bayesian-optimized XGBoost model achieves the best performance (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>R</mi><mo>=</mo><mn>0.9870</mn></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>RMSE</mi><mo>=</mo><mi>$</mi><mn>3.44</mn><mo>/</mo><msup><mi>km</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>NSE</mi><mo>=</mo><mn>0.9651</mn></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>KGE</mi><mo>=</mo><mn>0.8950</mn></mrow></semantics></math></inline-formula>), followed closely by CatBoost (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>R</mi><mo>=</mo><mn>0.9826</mn></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>RMSE</mi><mo>=</mo><mi>$</mi><mn>3.83</mn><mo>/</mo><msup><mi>km</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula>). …”
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