Work Package 1 – Coordination & Management

WP1 aims to ensure the successful implementation of the project in terms of time and budget. NOA is responsible for coordinating the activities, monitoring the implementation plan and data management, and reporting to ESA.

WP Leader: National Observatory of Athens (NOA)

Duration: March 2022 – March 2023

Work Package 2 – Big data acquisition and set-up of Data Cube

WP2 implements a Data Cube fit to the project needs. We will start from the definition of system requirements, specifications and data cube architecture, and then we will create labeled datasets with historical fires for the training and validation of our DL algorithms. Lastly, we will populate the Data Cube with space and non-space analysis ready data (ARD). We will develop scripts to automatically populate the data cube with satellite data, meteorological datasets from regional numerical weather prediction models, synoptic reanalyses, terrestrial ecosystem models, and in-situ observations. This WP is also responsible for the generation of terrestrial ecosystem model outputs.

WP Leader: Max Planck Institute for Biogeochemistry (MPG)

Duration: March 2022 – June 2022

Work Package 3 – Seasonal fire hazard prediction

WP3 aims at developing trustworthy data-driven deep learning architectures for seasonal fire hazard forecasting. As a first task, we will research multimodal EO data fusion techniques on top of data cubes. We will estimate diachronic trends in fused EO and non-space time-series of data related to forest fires and we will attempt to find teleconnection patterns between fire drivers at scales larger than Europe. Our second task is to develop a seasonal fire hazard focasting system coupling DL and generic Terrestrial Ecosystem Models that estimate carbon and water fluxes, and stocks. We will analyse time-series of climate-related, vegetation and anthropogenic indicators regarded as fire drivers, vis-a-vis a database of burnt areas in Europe for the past 20 years. Subsequently, we will apply computer vision techniques to gain insights on how our forecasting model works. We aim to interpret and explain the DL data-driven model and identify causal relations between covariates.

WP Leader: National Observatory of Athens (NOA)

Duration: June 2022 – December 2022

Work Package 4 – Prototype system development

WP4 aims to develop a prototype production system. We will create fully automatic processing chains, from data access and downloading to remote sensing data pre-processing, AI inference and final product generation. We will also develop a web-GIS platform, i.e., a front-end application to disseminate fire hazard forecasts. We will design and implement visual analytics to assess fire risk on assets (population, businesses, infrastructure, agricultural production, etc.) and we will create interactive plots to assist model explainability and provide insights for causal relationships.

WP Leader: Harokopio University of Athens (HUA)

Duration: December 2022 – March 2023

Work Package 5 – Dissemination, Communication & User Engagement

WP5 aims to bring visibility to the project through scientific publications in peer reviewed journals and the design of communication material to reach different stakeholder groups. It involves the implementation of the project’s communication strategy, the creation of the website and social media accounts and the development of AI explainability animations.

WP Leader: National Observatory of Athens (NOA)

Duration: March 2022 – March 2023