EGU23-3501, updated on 26 Jun 2024
EGU General Assembly 2023
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

EarthNets: An Open Deep Learning Platform for Earth Observation

Zhitong Xiong and Xiao Xiang Zhu
Zhitong Xiong and Xiao Xiang Zhu
  • Data Science in Earth Observation, Technical University of Munich, München, Germany

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

[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).

How to cite: Xiong, Z. and Zhu, X. X.: EarthNets: An Open Deep Learning Platform for Earth Observation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3501,, 2023.