Small Data, Big Maps: Training Geospatial ML Models When Samples Are Scarce
Towards Data Science1566 字 (约 7 分钟)
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The core bottleneck in geospatial ML is expensive field samples, not compute; solving small-sample issues requires increasing per-sample information density via multi-source feature engineering and prioritizing low-variance models like Random Forest to control overfitting.
入选理由：亚马逊雨林单个森林清查样地成本相当于一台ML训练计算机，实地标签稀缺是核心约束。
FeaturedArticle#Geospatial ML#Small Data#Feature Engineering#Random Forest#Remote Sensing英文