How to Improve the Reproducibility, Replicability, and Extensibility of Remote Sensing Research
How to Improve the Reproducibility, Replicability, and Extensibility of Remote Sensing Research
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DOI：
10.3390/rs14215471
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发表时间：
2022-10
期刊：
影响因子：
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通讯作者：
P. Kedron;Amy E. Frazier
中科院分区：
文献类型：
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作者：
P. Kedron;Amy E. Frazier
遥感领域发生了显着的变化，研究人员现在可以很容易地获得大量图像。新技术，如无人驾驶飞机系统，使任何预算适中的人都可以收集自己的遥感数据，方法创新增加了处理和分析数据的灵活性。这些变化创造了在空间背景、测量系统和计算基础设施中复制、复制和比较遥感方法和结果的机会和需要。复制和复制研究是理解研究的可信度和将最新进展推广到新发现的关键。然而，可再现性和可复制性(R&R)仍然是遥感领域的问题，因为许多研究不能独立地重新创建和验证。加强遥感研究的R&R将需要研究界投入大量的时间和精力。然而，使遥感研究可重复和可推广并不一定是一种负担。在本文中，我们将在遥感的背景下讨论R&R，并将该领域的最新变化与阻碍R&R的关键障碍联系起来，同时讨论研究人员如何克服这些障碍。我们主张在该领域发展两个研究流：(1)协调执行有组织的前瞻性复制序列，以及(2)引入可用于测试结果和方法的可复制性的基准数据集。
The field of remote sensing has undergone a remarkable shift where vast amounts of imagery are now readily available to researchers. New technologies, such as uncrewed aircraft systems, make it possible for anyone with a moderate budget to gather their own remotely sensed data, and methodological innovations have added flexibility for processing and analyzing data. These changes create both the opportunity and need to reproduce, replicate, and compare remote sensing methods and results across spatial contexts, measurement systems, and computational infrastructures. Reproducing and replicating research is key to understanding the credibility of studies and extending recent advances into new discoveries. However, reproducibility and replicability (R&R) remain issues in remote sensing because many studies cannot be independently recreated and validated. Enhancing the R&R of remote sensing research will require significant time and effort by the research community. However, making remote sensing research reproducible and replicable does not need to be a burden. In this paper, we discuss R&R in the context of remote sensing and link the recent changes in the field to key barriers hindering R&R while discussing how researchers can overcome those barriers. We argue for the development of two research streams in the field: (1) the coordinated execution of organized sequences of forward-looking replications, and (2) the introduction of benchmark datasets that can be used to test the replicability of results and methods.