Journalism is about observing, investigating and reporting on politically and socially important events, challenging issues and long-term developments. Especially when it comes to monitoring climate politics, journalists have become essential. But, (environmental) journalism highly relies on expert knowledge, data analysis skills and, most importantly, access to reliable large-scale information. The possibilities of raising public environmental awareness and thus tackling climate crisis also depend on whether this information is freely available and accessible for control, analysis and coverage by independent journalists as well as the general public.

The increasing availability of satellite footage and sensor data has facilitated new ways of storytelling and newsgathering under the name of “satellite journalism”’¹ and “sensor journalism“². Although, satellite and sensor journalism have been proven critical for verification, environmental monitoring and investigation, the use of Earth Observation (EO) imagery and data is still restricted with regard to access as well as with regard to understanding. In addition, EO data is often stored in separate data silos of diverse structure and quality. Free-of-charge or at least cost-effective access to satellite and EO data, with no restrictions and low technical barriers, is needed.

CALLISTO aims to provide direct access and easier use of EO data, and by this support and encourage journalistic investigation and reporting. In order to fully exploit the potential of EO data for journalistic storytelling and for establishing environmental awareness, EO data can be contextualised and put into reference with other sensor data (e.g., from air, sea and ground measurement stations), as well as with information stemming from traditional and social media. CALLISTO’s main goal is to develop easy-to-use monitoring tools that provide journalists with aggregated satellite data that can be cross-referenced with information from multiple other sources, such as various sensors and (social) media. Leveraging the benefits of state-of-the-art technologies (e.g., Deep Learning, Machine Learning, etc.), these tools will provide journalists with credible information concerning air quality, contributing to better environmental storytelling. Thus, CALLISTO will not only make satellite data accessible for and useable by journalists but also aims at boosting the increasingly relevant sector of data-driven, investigative and environmental journalism as well as verification. aims to provide direct access and easier use of EO data, and by this support and encourage journalistic investigation and reporting. In order to fully exploit the potential of EO data for journalistic storytelling and for establishing environmental awareness, EO data can be contextualised and put into reference with other sensor data (e.g., from air, sea and ground measurement stations), as well as with information stemming from traditional and social media. CALLISTO’s main goal is to develop easy-to-use monitoring tools that provide journalists with aggregated satellite data that can be cross-referenced with information from multiple other sources, such as various sensors and (social) media. Leveraging the benefits of state-of-the-art technologies (e.g., Deep Learning, Machine Learning, etc.), these tools will provide journalists with credible information concerning air quality, contributing to better environmental storytelling. Thus, CALLISTO will not only make satellite data accessible for and useable by journalists but also aims at boosting the increasingly relevant sector of data-driven, investigative and environmental journalism as well as verification.

Implementation: Deutsche Welle will exploit the project results by expanding the use of satellite footage and data from sensors for journalistic research and verification, and by potentially implementing new technologies in editorial departments.

¹https://reutersinstitute.politics.ox.ac.uk/our-research/satellite-journalism-big-picture

²https://www.redalyc.org/jatsRepo/649/64948471005/html/index.html