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Earth observation (EO) data are critical for monitoring the state of planet Earth and can be helpful for various real-world applications [1]. Although numerous benchmark datasets have been released, there is no unified platform for developing and fairly comparing deep learning models on EO data [2]. For deep learning methods, the backbone networks, hyper-parameters, and training details are influential factors while comparing the performances.. However, existing works usually neglect these details and even evaluate the performance with different training/validation/test dataset splits. This makes it difficult to fairly and reliably compare different algorithms. In this study, we introduce the EarthNets platform, an open deep-learning platform for remote sensing and Earth observation. The platform is based on PyTorch [3] and TorchData. There are about ten different libraries, covering different tasks in remote sensing. Among them, Dataset4EO is designed as a standard and easy-to-use data-loading library, which can be used alone or together with other high-level libraries like RSI-Classification (for image classification), RSI-Detection (for object detection), RSI-Segmentation (for semantic segmentation), and so on. Two factors are considered for the design of the EarthNets platform: the first one is the decoupling between dataset loading and high-level EO tasks. As there are more than 400 RS datasets with different data modalities, research domains, and download links, efficient preparation of analysis-ready data can largely accelerate the research for the whole community. The other factor is to bring advances in machine learning to EO by providing new deep-learning models. The EarthNets platform provides a fair and consistent evaluation of deep learning methods on remote sensing and Earth observation data [4]. It also helps bring together the remote sensing and a larger machine-learning community. The platform, dataset collections are publicly available at https://earthnets.github.io.[1] Zhu, Xiao Xiang, et al. "Deep learning in remote sensing: A comprehensive review and list of resources." IEEE Geoscience and Remote Sensing Magazine 5.4 (2017): 8-36.[2]Long, Yang, et al. "On creating benchmark dataset for aerial image interpretation: Reviews, guidances, and million-aid." IEEE Journal of selected topics in applied earth observations and remote sensing 14 (2021): 4205-4230.[3] Paszke, Adam, et al. "Pytorch: An imperative style, high-performance deep learning library." Advances in neural information processing systems 32 (2019).[4] Xiong, Zhitong, et al. "EarthNets: Empowering AI in Earth observation." arXiv preprint arXiv:2210.04936 (2022). |
