SAFERS Project - Land Cover Map
- Creators
- LINKS Foundation
Description
The SAFERS project aims to develop a comprehensive, emergency management system for the management of forest fires. The project is funded by the European Union's Horizon 2020 research and innovation programme under Grant Agreement No. 869353.
Abstract
Changing climate is increasing the risk of wildfires. As temperatures continue to soar in the future, scientists warn that extreme fires will become more common. Meaningful early warning forecasts, early identification and tracking as well as effective response are of paramount importance to save lives and contain environmental damage. The EU-funded SAFERS project will develop a complex emergency management system capable of acting along the whole emergency management cycle, thanks to the coupled use of heterogeneous Big Data, advanced models, and AI. Earth Observation data from Copernicus and GEOSS will be the primary data source, which will be combined with data from social media, smoke detectors, and mobile applications. This will allow first responders, citizens and decision makers to generate new and more accurate information, enhancing our society's resilience against wildfires.
Technical info
Land cover (LC) segmentation plays a critical role in various applications, including environmental analysis and natural disaster management. However, generating accurate LC maps is a complex and time-consuming task that requires the expertise of multiple annotators and regular updates to account for environmental changes. In this work, we introduce SPADA, a framework for fuel map delineation that addresses the challenges associated with LC segmentation using sparse annotations and domain adaptation techniques for semantic segmentation. Performance evaluations using reliable ground truths, such as LUCAS and Urban Atlas, demonstrate the technique's effectiveness. SPADA outperforms state-of-the-art semantic segmentation approaches as well as third-party products, achieving a mean Intersection over Union (IoU) score of 42.86 and an F1 score of 67.93 on Urban Atlas and LUCAS, respectively.