FIPAS | Forest Fire Prediction

The FIPAS PLATFORM is an advanced ecosystem integrating artificial intelligence, machine learning and a geospatial analysis system, designed for the prediction and early warning of forest fires. Its aim is to provide the organisations responsible for fire prevention and management with tools for fast, accurate and coordinated decision-making.

FIPAS Index


A composite metric that assesses fire risk in 1 km² cells, providing both real-time values and 7-day projections. The calculation is based on the weighted combination of four predictive models:

Climate model

Current and forecast weather factors, including temperature, humidity, wind speed and direction.


Natural-causes model

Includes lightning detection and risk estimation associated with natural ignitions, using Linet data and the Fuel Water Index (FWI).


Human-intervention model

Considers proximity to infrastructure such as roads, railway lines, trails, power lines and recreational areas, with data obtained from structured geospatial sources.


Socioeconomic model

Human and economic factors associated with activity in the area, with potential impact on ignition probability.


The system not only calculates a global index, but also provides the individual prediction of each submodel. It also estimates the potential number of wildfires (IIFF) for each zone and period.

The main data sources used include Sentinel satellites (ESA), MODIS and VIIRS sensors (NASA/NOAA), Google Earth Engine with thermal-anomaly and hotspot detection products, as well as weather data from AEMET and OpenMeteo. In addition, local ground measurements are integrated and, soon, data provided by a structured citizen-collaboration application.

The validation of events and detections is carried out through spatial and temporal fusion between the various sources, confirming fires only if there is a match across two or more detection channels.

As part of its technological evolution, FIPAS is incorporating the use of HAPS stratospheric platforms (High Altitude Pseudo-Satellites) to improve detection coverage and enable, in collaboration with technology partners, the replication of mobile-phone signal from space. This development aims to strengthen both remote-sensor connectivity and emergency communications in remote areas.

FIPAS Main Remote-Sensing Dashboard

FIPAS Event Data Dashboard

Satellite image of the event up to 30 cm

Virtual Goggles from the Advanced Command Post (PMA)