![]() This project is known as the Earthnet Data Assessment Pilot (EDAP). Some of these new missions are potential candidates for Earthnet TPMs, and ESA have therefore set up a project to assess the quality and the suitability of these missions and also to establish dialogues with the various mission providers in order to improve the overall coherence of the EO system. These New Space players are now portraying an important role in the EO international strategy. In recent years the availability of low cost small satellites and the innovation of constellations resulted in an increased number of commercial companies who have established business models to provide information services fed by their own satellite systems. In line with the Earthnet Programme objectives first established in 1977, ESA aims to foster cooperation and collaboration with not only other national space agencies, but also commercial mission providers. Complementary to ESA-owned EO missions, the programme allows European users access to a large portfolio of TPM and is particularly important for promoting the international use of EO data. Third Party Missions (TPM), into the overall ESA Earth Observation (EO) strategy. This role involved providing the framework for integrating non-ESA missions, i.e. X.: EarthNets: An Open Deep Learning Platform for Earth Observation , EGU General Assembly 2023, Vienna, Austria, 24–, EGU23-3501,, 2023.For over 40 years ESA’s Earthnet Programme has played a significant role as part of ESA’s mandatory activities, being a major contributor to the Global Earth Observation System of Systems (GEOSS). ![]() "EarthNets: Empowering AI in Earth observation." arXiv preprint arXiv:2210.04936 (2022). "Pytorch: An imperative style, high-performance deep learning library." Advances in neural information processing systems 32 (2019). "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. "Deep learning in remote sensing: A comprehensive review and list of resources." IEEE Geoscience and Remote Sensing Magazine 5.4 (2017): 8-36. The platform, dataset collections are publicly available at. It also helps bring together the remote sensing and a larger machine-learning community. The EarthNets platform provides a fair and consistent evaluation of deep learning methods on remote sensing and Earth observation data. The other factor is to bring advances in machine learning to EO by providing new deep-learning models. 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. 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. 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. There are about ten different libraries, covering different tasks in remote sensing. The platform is based on PyTorch and TorchData. In this study, we introduce the EarthNets platform, an open deep-learning platform for remote sensing and Earth observation. This makes it difficult to fairly and reliably compare different algorithms. However, existing works usually neglect these details and even evaluate the performance with different training/validation/test dataset splits. For deep learning methods, the backbone networks, hyper-parameters, and training details are influential factors while comparing the performances. Although numerous benchmark datasets have been released, there is no unified platform for developing and fairly comparing deep learning models on EO data. ![]() Earth observation (EO) data are critical for monitoring the state of planet Earth and can be helpful for various real-world applications. ![]()
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