What is
The models use satellite data, historical data, and environmental indicators to analyze the evolution of phenomena over time and detect risk signals in advance.
The block is modularly structured: it can be adopted as a whole or through individual modules dedicated to specific environmental hazards, such as fire, flooding or landslides, depending on the needs of the application context.
It is designed to feed into monitoring dashboards already present in organizations, adding predictive capability to systems that are often only descriptive.
What kind of problems does it address
Many environmental monitoring systems are limited to displaying the current state of the territory, without supporting the prediction of how phenomena may evolve. This module enables a shift from a static reading to a dynamic risk assessment, supporting prevention, planning, and control activities.
Use cases already developed and tested
The module includes three use cases that have already been implemented. Wildfires: predictive models that combine satellite and environmental data to identify risk conditions and support preventive monitoring. Flooding: predictive analysis to anticipate critical situations and support emergency management. Landslides: territory monitoring through satellite data to detect signs of instability in high-risk areas.
Why is it reusable
The models share a common analytical structure but are organized into independent modules that can be activated individually or combined with one another. In addition to modularity, the structure can be adapted by varying satellite and territorial data sources, risk indicators, analysis time windows, thresholds, and interpretation rules. This makes it possible to extend the module to new environmental scenarios while maintaining the existing framework.
Customer benefits
Predict, not just observe
Models make it possible to anticipate the evolution of environmental phenomena, relying on satellite data and algorithms already tested in the field
Enhancing what already exists
Predictive capabilities plug into existing monitoring dashboards, reinforcing them without requiring new platforms or invasive replacements
Moving from reaction to prevention
Decision support enables planning of interventions and priorities before critical issues become emergencies
Growing by measurable steps
Adoption can start with individual use cases or demos, and then gradually expand to other scenarios and levels of complexity.