Harnessing AI, GIS, and Earth Observation for Early Warning Systems

As the global community confronts an era of interconnected risks—climate volatility, health crises, ocean degradation, and economic disruptions—Early Warning Systems (EWS) stand out as pivotal, forward-looking solutions for managing uncertainty and safeguarding sustainable development gains. This virtual side event convened by the Global Centre for Risk and Innovation (GCRI) examines how the strategic integration of Artificial Intelligence (AI), Machine Learning (ML), Geographic Information Systems (GIS), and Earth Observation (EO) is transforming EWS from reactive mechanisms to anticipatory, high-impact infrastructures that propel the 2030 Agenda.

Why Now?
Traditional risk monitoring and response frameworks are straining under the weight of compound shocks. More than ever, development actors require real-time intelligence, cross-domain data analytics, and automated response triggers that translate signals into early interventions. By harnessing rapidly advancing technologies, EWS can be reimagined as data-driven backbones for resilience-building and accelerated SDG progress.

Scope of the Session

1. AI/ML-Enhanced Risk Intelligence
   • Predictive modeling for multi-hazard scenarios, from extreme weather events to pandemic outbreaks.
   • ML algorithms for signal detection in complex, evolving data environments.
2. GIS and Integrated Geospatial Analysis
   • Real-time mapping for exposure, vulnerability, and resource allocation.
   • Geospatial frameworks that inform decision-making at local, national, and transboundary levels.
3. Earth Observation for Planetary Health
   • Monitoring of environmental parameters—including coastal habitats, forest cover, and atmospheric conditions—to detect anomalies before they escalate.
   • Harmonizing EO-derived data with on-ground measurements for holistic ecosystem intelligence.
4. Institutional Architecture and Policy Coherence
   • Aligning EWS outputs with national and global policy instruments, ensuring that advance warnings prompt real-world, time-sensitive actions.
   • Building robust multi-stakeholder collaborations spanning governments, IGOs, finance, academia, and communities.