![]() ![]() It is the place to ask questions, share ideas. You can join the climate action movement by signing up with EarthNet today. EarthNet is a global network of people and organizations dedicated to climate and ecological transformation. Retrofits Hub features forums to ask questions, connect with experts, share ideas and resources, promote events and more. This platform will facilitate information sharing on deep building retrofits needed to respond to the climate crisis. EarthNets supports standard dataset libraries and cutting-edge deep learning models to bridge the gap between remote sensing and the machine learning community. Alongside the launch of EarthNet came the launch of their Retrofits Hub. ![]() EarthNet will provide a digital space for such collaborative efforts, previously a gap within the climate movement. According to EarthNet, “we exist to break down institutional silos, improve knowledge sharing, and, generally, enhance beautiful collaboration between organizations and people working for a healthy, equitable, and sustainable future.” The climate crisis is a complex problem that requires global collaboration to inspire climate action. X.: EarthNets: An Open Deep Learning Platform for Earth Observation , EGU General Assembly 2023, Vienna, Austria, 24–, EGU23-3501,, 2023.Launched only days ago, EarthNet is a new initiative that aims to galvanize climate action using social network technology. "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. EarthNets 62 followers Germany Overview Repositories Projects Packages People Pinned lightning-flash-earthnets Public Forked from Lightning-Universe/lightning-flash EarthNets with the Lighning - Flash integrator Python Dataset4EO Public Officail Repo for the EarthNets Platform. It also helps bring together the remote sensing and a larger machine-learning community. EarthNet is a network for accelerating climate action by making it easier to create a project. The EarthNets platform provides a fair and consistent evaluation of deep learning methods on remote sensing and Earth observation data. Create a project, build a network, and get things done. ![]() 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. 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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