MAPEAMENTO DA SOJA POR INTERPRETAÇÃO VISUAL E RANDOM FOREST: COMPARAÇÃO DE CONCORDÂNCIA E EFICIÊNCIA EM RIO VERDE (GO)
比较Sentinel-2大豆人工目视解译与随机森林分类的精度与效率。
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Random Forest-Based Vegetation and Non-Vegetation Classification in Siak Regency Using Landsat 8 and Google Earth Engine
用Landsat 8影像和随机森林在GEE中分类Siak县植被与非植被。
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Probabilistic Multi-Temporal Land Use and Land Cover Change Analysis Using Random Forest and Sentinel-2 Data in Haridwar, India
用随机森林与Sentinel-2多时相影像分析印度哈里德瓦尔2017-2025年土地利用变化。
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Remote Sensing and Machine Learning for Drought Assessment: The Role of Aerodynamic Drivers in Ceará, Brazil
用随机森林与遥感数据构建巴西塞阿拉州帕尔默干旱指数的时空模型。
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Integrating Proximal and Remote Sensing to Improve Characterization and Mapping of Soil Organic Carbon Stocks under Contrasting Land Uses
结合近地与遥感数据及随机森林预测并制图巴西农场不同土地利用下的土壤有机碳储量。
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