Don’t throw the baby out with the bathwater: reappreciating the dynamic relationship between humans, machines, and landscape images
Don’t throw the baby out with the bathwater: reappreciating the dynamic relationship between humans, machines, and landscape images
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不要把婴儿和洗澡水一起倒掉：重新欣赏人、机器和风景图像之间的动态关系
DOI：
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发表时间：
2020
影响因子：
5.2
通讯作者：
Raechel A. Portelli
中科院分区：
文献类型：
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作者：
Raechel A. Portelli
人类对地球的观察促进了我们对塑造地貌的物理模式和过程的理解。随着时间的推移，科学解释的行为已经转变为通过机器进行调解的行为，在观察者和被观察者之间创造了距离。机器学习正在扩大这一差距，并改变我们获得关于世界的知识的方式。提出这样一个问题：以牺牲人类视觉解释为代价推进机器学习会有什么损失吗？认识到这些计算算法在处理海量、异质和动态生态数据集方面的有效性，科学家不应放弃人类智能在理解景观格局、过程和关系方面的重要贡献。本文回顾了社会、文化、政治或军事对人类与景观遥感图像之间关系的影响。这篇综述突出了自动机器学习方法和人类解释之间的紧张关系。建议通过使用交互式、可视显示和开发透明的机器学习方法，支持使用人机一体化系统。在部署机器学习算法时，人类分析员应该在景观生态应用程序的设计中保持核心地位。人和机器在数据处理方面的优势互补表明，关于模式和过程的最具信息量的见解可以发生在精心设计的人在环路系统的实施中。
The observation of the earth by humans has advanced our understanding of the physical patterns and processes that shape the landscape. Over time, the act of scientific interpretation has transformed into one mediated through machines, creating distance between the observer and the observed. Machine learning is expanding this gap and transforming how we gain knowledge about the world. Raising the question is there something to be lost by advancing machine learning at the expense of human visual interpretation? Recognizing the usefulness of these computational algorithms for dealing with massive, heterogeneous, and dynamic ecological datasets, scientists should not abandon the important contributions of human intelligence to understanding landscape patterns, processes, and relationships. This paper presents a review of social, cultural, and political or military influences on the relationship between humans and remote sensing images of the landscape. This review highlights tensions between automated machine learning approaches and human interpretation. Support for the use of human–machine integrated systems through the use of interactive, visual display, and the development of transparent machine learning methods is suggested. The human analyst should remain central in the design of landscape ecology applications when deploying machine learning algorithms. The complementary strengths of the human and machine in data processing suggest that the most informative insights regarding pattern and process can happen in the implementation of carefully designed Human in the Loop systems.