Don't throw the baby out with the bathwater: reappreciating the dynamic relationship between humans, machines, and landscape images

摘要

Context 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? Objectives 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. Methods 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. Results 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. Conclusions 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.