# Democratizing Deep Learning Applications in Earth and Climate Sciences on the Web: EarthAlHub
## 1 Introduction
Using the boilerplate of a popular deep learning package, Keras [27], Keras.js provides an easy-to-use framework to train and run deep learning models. While it was one of the go-to solutions to deploy some deep learning models on the web when it was first released, it is not actively maintained as of now in favor of TensorFlow.js. TensorFlow.js uses the TensorFlow library, which is backed by Google. Now that Keras models and TensorFlow models are somewhat intertwined, TensorFlow.js supports both Keras and TensorFlow models, which covers a great majority of deep learning models, but not as much of the applicable research. Beyond this, TensorFlow.js has the large functionality of Keras and TensorFlow in terms of available architectures and matrix operations. This makes TensorFlow.js the most popular deep learning framework for online applications. Similar to ONNX.js, TensorFlow.js also supports WebAssembly and WebGL for faster training and execution. Furthermore, there are a few other deep learning frameworks that run on JavaScript and subsequently could be used on web browsers, such as WebDNN [28], ConvNet.js [29], and brain.js [30], but they do not focus on community support and practicality like TensorFlow.js. One exception might be ml5.js [31], which runs on TensorFlow.js and aims to provide an abstraction layer to make running models on the web more accessible, thus differentiating itself from others in terms of audience and value. There are vast quantities of deep learning application papers published every day. Most of the time, the research is not easily reproducible. Even when the code or pre-trained models are provided, since all deep learning frameworks need some level of expertise to run a model, people with no AI modeling experience will not be able to run and see the results of a study for themselves. Deep learning has been a methodology of interest for many earth science studies. Whether it be about social media streams, such as Twitter data, that is related to natural disasters [32], or digital elevation models [33], there are many studies in earth science that utilize deep learning methods. Modeling floods for prediction [34; 35] and rainfall to increase temporal interpolation [36] are among the deep learning application studies in the field, along with benchmark dataset efforts [37] to support the literature [38; 39]. Even though deep learning application studies are somewhat in abundance in the literature of earth and climate sciences, there are still challenges in the scientific communication of studies utilizing deep learning. To the best of our knowledge, there are no studies that have shared their developed and trained deep learning models for earth and climate sciences problems on the web for third parties to experience. Beyond already published studies,similar to any data-driven methodology, adopting deep learning in their applications is appealing to domain scientists, i.e., scientists who are experts in earth and climate sciences, but might not have the technical expertise to apply deep learning in their field. In order for them to start exploring how deep learning could be utilized in their fields, understanding the literature beyond what the scientific literature presents is of the utmost importance to capitalize on the deep-learning-based modelling potential in the field. This study aims to empower scientists studying in the domains of earth and climate sciences to reproduce and test previously trained deep learning models easily with their data and settings without having to train or even know how to use a specific tensor computation library used in selected projects. We present EarthAIHub, an easy-to-use web application utilizing client-side resources to run deep learning applications. EarthAIHub comes with a configuration middleware between individual models and TensorFlow.js to facilitate the easy adoption of the system among deep learning practitioners.