# Democratizing Deep Learning Applications in Earth and Climate Sciences on the Web: EarthAlHub
## 1 Introduction  
One of the main issues regarding applicable deep learning research is the availability of the models, case studies, and research in an easy and accessible way to other researchers [1]. While reproducing results has been possible, albeit both time and resource-intensive, for experts in the field, it remains a problem for non-technical people, researchers, and students who are new to this domain to see how well a deep learning model performs over their own inputs. This is mostly due to either computational limitations or allocating the time needed for setting up and testing many models they might consider for their research. Many studies [2; 3] have made additional efforts to support scientific communication and reproducibility by providing a working example of their study in a web-friendly format that, more often than not, accepts inputs from users and returns an output. Such applications typically carry some popular science and outreach value. Another approach used by deep learning researchers to communicate their findings to the public is to provide their code and trained deep learning models via platforms, such as GitHub, and various deep learning model repositories, often called model zoos (i.e., ONNX Model Zoo [4], Nvidia's NGC Catalog [5]). While most studies adopt the latter method to enable other people to try their research, this tendency has a logistically sound reason. Even though it is vital for the public to be able to see how the findings of some research are realized in real-world settings, it is also difficult to provide an end product that employs the presented research for third parties to use. Providing proof of concept for research is the backbone for any study to be usefuland accessible to others. It is also resource-consuming for researchers to transform that proof of concept into a working application for real-world use cases most of the time. The resource-wise needs for an application typically comprise many aspects, from labor to computational power. From a researcher's perspective, it is more sensible to utilize those resources in further research instead of allocating them for real-world applications just for publicity. One could argue that computational power needs could be surpassed by simply implementing a web application that utilizes the client side's computational power. This would be a reasonable approach in order to get past the problem of devoting costly computational capabilities, but it also adds to the need for labor since, typically, a deep learning model is developed in an experimental setting in server-side programming languages such as Python. Consequently, researchers typically prefer to release the code that could be used to reproduce the results, sometimes alongside a pre-trained model. This, in theory, is an attempt to communicate the study's findings, but by its nature, it only communicates with other researchers who are willing to put the necessary time in to advance the science, rather than supporting non-technical people who might be interested in, and beyond that, could benefit from, the conducted research. Most of the time, these studies share their model codes on platforms such as GitHub, linking their pre-trained models stored in the cloud. Beyond that, there are many \"model zoos\" from which many pre-trained state-of-the-art models for common tasks can be acquired. These zoos occasionally include the code and sometimes give out instructions to show how to utilize the given model. While there are examples of model zoos that are framework-blind, in other words, model zoos that do not have specific framework requirements, such as modelzo.co [6], most numeric computing libraries also have their own zoos in their repositories, such as TensorFlow [7] and Open Neural Network Exchange [4]. While these zoos are community-driven efforts, there are some model zoos that list ready-to-use deep learning models in specific commercial AI devices, such as Texas Instruments' TI Edge AI Cloud [8] and Hailo.ai's Model Zoo [9].