Enhancing Reproducibility and Replicability in Remote Sensing Deep Learning Research and Practice
Enhancing Reproducibility and Replicability in Remote Sensing Deep Learning Research and Practice
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DOI：
10.3390/rs14225760
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发表时间：
2022-11
期刊：
影响因子：
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通讯作者：
Aaron E. Maxwell;Michelle S. Bester;Christopher A. Ramezan
中科院分区：
文献类型：
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作者：
Aaron E. Maxwell;Michelle S. Bester;Christopher A. Ramezan
许多问题会降低深度学习（DL）在遥感研究和应用中的再现性和可复制性，包括架构的复杂性和可定制性、变量模型训练和评估过程和实践、无法完全控制建模工作流的随机组件、数据泄漏、计算需求以及过程的固有性质，这是复杂的，难以系统地执行，并且具有挑战性。本文讨论了与遥感中基于卷积神经网络（CNN）的深度学习相关的关键问题，用于进行语义分割、对象检测和实例分割任务，并为增强再现性和可复制性以及后续研究结果、提议的工作流程和生成的数据的效用提供了最佳实践建议。我们还强调了研究人员在试图提高实验的可重复性和可复制性时面临的遗留问题和挑战。
Many issues can reduce the reproducibility and replicability of deep learning (DL) research and application in remote sensing, including the complexity and customizability of architectures, variable model training and assessment processes and practice, inability to fully control random components of the modeling workflow, data leakage, computational demands, and the inherent nature of the process, which is complex, difficult to perform systematically, and challenging to fully document. This communication discusses key issues associated with convolutional neural network (CNN)-based DL in remote sensing for undertaking semantic segmentation, object detection, and instance segmentation tasks and offers suggestions for best practices for enhancing reproducibility and replicability and the subsequent utility of research results, proposed workflows, and generated data. We also highlight lingering issues and challenges facing researchers as they attempt to improve the reproducibility and replicability of their experiments.