Sinergise sponsoring FOSS4G 2023

Sinergise is a Slovenian software company developing large-scale geospatial systems for use in land administration, agriculture policy support and Earth observation. From its offices in Ljubljana and Graz it serves clients all over the world — from nearby countries such as Croatia, Serbia, Macedonia, and Montenegro to France, the United Kingdom, Scotland, Azerbaijan, Moldova, Ghana, Nigeria, Tanzania and Mauritius. Its solutions are used by more than 2 million people annually and help governments and companies manage assets worth trillions of EUR.

Sinergise is best known for its award-winning service Sentinel hub - a cloud API for archiving, processing and distributing satellite data from a large variety of sources. It uses cloud infrastructure and innovative algorithms to efficiently process and distribute data, generating user-defined analysis-ready datasets in seconds. The API can be easily and cost-effectively integrated into any mapping or web application, data extraction process or machine learning workflow. Sentinel hub streamlines the common workloads required to work with satellite images, freeing application developers to focus on added value services and end-user applications.

Sinergise is on a mission to promote and leverage geospatial information to benefit society. They aim to use information, extracted from the vast volumes of Earth observation imagery that are becoming available every day, to help protect the environment, mitigate climate change and improve people’s everyday lives. All along the way, Sinergise is sharing their findings and lessons learned through blogs and open-source tools and libraries as well as encouraging and engaging the community to do similar things. Their open-source portfolio includes EOBrowser, a web application to explore satellite imagery from various sources; s2cloudless, the leading cloud-detection package for Sentinel 2 data; eo-learn, a library that brings Python and remote sensing closer together; and eo-grow, a framework for scaled-up processing in Python.