In the first period of the project, eo-learn has grown into a remarkable piece of open-source software, ready to be put to use by anyone who is curious about EO data, to actually using them for data science and machine learning. We did a blog series on land use and land cover classification, using eo-learn, which have accumulated more than 28 thousand views so far. On GitHub , open-source repository, where eo-learn is available for download, the project has over 230 stars and 35 developers are subscribed to notifications about changes of eo-learn. More than 60 developers have created their own branch of eo-learn, which people do when they want to do local modifications. These numbers do not include people, who use eo-learn as a library installed like any other Python package. The eo-learn repository has more than 2.500 views in a bi-weekly period coming from more than 400 unique users.
During the second reporting period from April 2019 to June 2020 the focus of the project was the development of PerceptiveSentinel core libraries, which have been upgraded with the introduction of pre-processing algorithms incorporated into eo-learn library. The library illustrates high-level architecture and main components of PerceptiveSentinel. It is exposed as a rich variety of pre-processing and processing algorithms used for data pre-processing or creation of custom EO-processing chains such as data pre-processing, deep learning algorithms incorporated into processing library. These are integrated with EO-QMiner to support streaming machine learning, new data gateways and cases showing how to use eo-learn library with external services. Software components eo-learn library, QMiner, pre-processing library and Jupyter Notebook have been integrated with PerceptiveSentinel platform (eo-learn platform). EO-QMiner integration layer has been developed to support seamless communication with machine-learning engine. Its main tasks are to transform data into existing QMiner data formats for the ingestion by EO-QMiner and to provide labelled data. Workflow designer and workflow engine have been upgraded with the addition of new input modules to support new data gateways and new processing modules to support streaming machine learning. Platform provides learning and interpretation data; external services provide interpreted data.
During the second period of the project On PyPI, where eo-learn is available for download, the project has over 28.000 downloads. More than 175 developers have created their own branch of eo-learn, which people do when they want to do local modifications. These numbers do not include people, who use eo-learn as a library installed like any other Python package. The eo-learn repository has more than 400 downloads per week. The Sentinel Hub forum thread dedicated to our open-source libraries such as eo-learn has over 120 threads, which were viewed more than 40 thousand times. There are numerous Sentinel Hub users, Enterprise level subscribers, for whom we know they are using it due to eo-learn and they generate more than 80% of the volume of the processed requests, more than quarter of a billion requests per month. The Sentinel Hub blog has 100.000 views of blog posts related to eo-learn, e.g. Introducing eo-learn, Land Cover Classification with eo-learn, Land Cover Monitoring.
