Innovation in earth observation and machine learning are advancing remote damage assessment in conflict zones. In response to escalating conflicts and the growing demand for humanitarian data, the Decentralized Damage Mapping Group (DDMG) emerged to coordinate efforts and address critical information gaps. As more information is shared in conflict zones, understanding misinformation, strengths of datasets, and limitations of analyses is increasingly important to navigate data-informed humanitarian decisions based on damage in conflict zones.

Drawing on use cases in Ukraine, Gaza, Lebanon and beyond, members of the group will share how varying approaches to different remote damage-detection methodologies impact data outputs, and how to interpret damage detection results to understand the impact of conflict on affected populations.

Come ready to discuss your use of damage analyses, collaboration ideas, and how data analysts and researchers can increase data transparency and openness in such sensitive contexts. This session is intended for humanitarian practitioners, remote sensing scientists/academics/analysts, government officials/intergovernmental organizations, and anyone interested in mapping conflict damage.