论文标题
地球观察深度学习的当前趋势：图像分类的开源基准测试领域
Current Trends in Deep Learning for Earth Observation: An Open-source Benchmark Arena for Image Classification
论文作者
论文摘要
我们提出AITLAS：基准竞技场 - 一个开源基准测试套件，用于评估地球观察中图像分类的最新深度学习方法（EO）。为此，我们对从十个不同的最先进的体系结构得出的500多个模型进行了全面的比较分析，并将它们与来自具有不同尺寸和属性的22个数据集的各种多类和多标签分类任务进行了比较。除了完全在这些数据集上训练的模型外，我们还基于在转移学习的背景下进行培训的模型，利用预先训练的模型变体，因为它通常在实践中执行。所有提出的方法都是一般的，可以轻松地扩展到本研究中未考虑的许多其他遥感图像分类任务。为了确保可重复性并促进更好的可用性和进一步的发展，所有实验资源在内的所有实验资源，包括训练有素的模型，模型配置以及数据集的处理详细信息（其相应的拆分用于培训和评估模型）均在存储库上公开可用：
We present AiTLAS: Benchmark Arena -- an open-source benchmark suite for evaluating state-of-the-art deep learning approaches for image classification in Earth Observation (EO). To this end, we present a comprehensive comparative analysis of more than 500 models derived from ten different state-of-the-art architectures and compare them to a variety of multi-class and multi-label classification tasks from 22 datasets with different sizes and properties. In addition to models trained entirely on these datasets, we benchmark models trained in the context of transfer learning, leveraging pre-trained model variants, as it is typically performed in practice. All presented approaches are general and can be easily extended to many other remote sensing image classification tasks not considered in this study. To ensure reproducibility and facilitate better usability and further developments, all of the experimental resources including the trained models, model configurations, and processing details of the datasets (with their corresponding splits used for training and evaluating the models) are publicly available on the repository: https://github.com/biasvariancelabs/aitlas-arena
