As geospatial machine learning models and maps derived from their predictions are increasingly used for downstream analyses in science and policy, it is imperative to evaluate their accuracy and applicability. Geospatial machine learning has key distinctions from other learning paradigms, and as such, the correct way to measure performance of spatial machine learning outputs has been a topic of debate. In this paper, I delineate unique challenges of model evaluation for geospatial machine learning with global or remotely sensed datasets, culminating in concrete takeaways to improve evaluations of geospatial model performance.
翻译:随着基于地球观测的机器学习模型和由其预测生成的地图在科学和政策领域中的下游应用越来越多,评估其准确性和适用性变得至关重要。地理空间机器学习的学习范式与其他学习范式有着关键的不同之处,因此正确地衡量空间机器学习输出的性能一直是争论的话题。在本文中,我概述了在使用全球或远程感测数据的地理空间机器学习模型时,模型评估面临的独特挑战,总结了提高地理空间模型性能评估的具体要点。