Objectives of the service
The service objective is to provide bespoke satellite-based analyses to support news agencies in their news reporting activities on humanitarian and environmental crises.
Especially in conflict or crisis situations, the available information is indeed scattered and disconnected, witnesses can be scarce, remote areas can be difficult to access and safety concerns can prohibit the presence of independent monitors. As a result, journalists find it difficult to closely monitor the events, due to the lack of sufficiently consistent, comprehensive, and comparable data.
GMV service will use the latest available imagery, as well as historical images, to detect and classify as much actionable evidence as possible from space. To that purpose, the service will leverage advanced Earth Observation (EO) and Machine Learning (ML) techniques and validated crowdsourced non-EO data to support the analyses, when made available by the customer.
The service geospatial products will be made available along with the EO data and products in a web portal for the users to get a more comprehensive picture.
It is important to highlight that GMV will only provide satellite-based objective and quantifiable products, leaving the analyses of the scenarios and the interpretation of the situation to the journalists.
Users and their needs
GMV service is targeted at digital media outlets and news agencies dealing with investigative, storytelling and analytical journalism.
A sample of needs shared by stakeholders interviewed by GMV are:
Have access to geospatial products and EO imagery that is easily "readable", to be able to identify features under analysis;
Use EO data to provide unbiased and independent facts to complete and corroborate witnesses’ stories, to lend credibility to a story and add transparency of reporting, especially in remote and inaccessible locations;
Use EO data to perform spatio-temporal analyses to monitor phenomena on a macro scale (e.g., regional- or country-level) and bring to light hidden cause-effect mechanisms;
Address the issue of scattered and disconnected information;
Have access to a tool to visualise, download and explore the EO data and geospatial products.
During the project, a use case study will be implemented to showcase the service under development to a real-world scenario. The use case study has been pinpointed by the anchor user as a high-priority application and is expected to provide critical input information for their news reports.
Service/ system concept
The service will combine remote sensing expertise with state-of-the-art deep learning methods to generate valuable data products from raw EO data. The data products will be available to be visualised, explored, and interrogated by the users on a web portal.
Satellite imagery is the core source of data for the generation of the information products, and the backend system is a suite of information extraction and enrichment pipelines. The below diagram depicts the information flow throughout the service.
Space Added Value
The space assets considered in the use case study are:
Optical satellite imagery (Sentinel-2 time series);
Very High Resolution (VHR) optical imagery;
Global Navigation Satellite System data (e.g., GPS).
The envisaged processing chain is developed to scan large spatial areas and long-time spans of the Sentinel-2 archive and automatically identify and extract the feature under analysis.
Sentinel-2 time series data is complemented by VHR imagery to train the machine learning models and validate the results, together with ground truth data (where available).
Journalists will be able to work with the EO products and integrate them together with their other sources of information (local information, testimonies, etc.). All these pieces of information, as pieces in a puzzle, will help them understand the scenario under investigation. In addition, satellite-based products and data could contribute to discover certain hidden cause-effect mechanisms (e.g., environmental degradation) and to assess the impact of environmental policies (e.g., banning of plastic bags).
The below diagram provides an overview of the value chain in the service.
The project is a CCN activity that aims to enhance the suite of BIGMIG services with a new space-based service.
The CCN activity kicked off at the end of June 2021. Since then, BIGMIG team held interviews with journalists from different media and enquired about the use of EO data in their daily work. These interviews helped GMV consolidate the user needs of the sector.
Three remote User Meetings were held with the anchor user to define their requirements and identify a high-priority application that can be showcased during the pilot phase. It is expected that the selected use case study will provide critical information for their news reports.
The new service has been designed to leverage the system and service architecture developed during BIGMIG main activities as much as possible. Nevertheless, the team has been busy evolving existing components and developing new ones where necessary. In particular, the algorithms to extract the feature under analysis in the use case have been developed.
The FAT-SAT milestone is scheduled for December 2022.