We don’t just live with risk: we anticipate it
We live with natural hazards every day: earthquakes, landslides, extreme rainfall.
Chile is a natural laboratory to observe, understand, and evaluate these processes in real conditions.
Cross-basin transfer in the Andes Mountain Range, Metropolitan Region, Chile: Trained in Maipo / Validated in Yerba Loca / No retraining required.
Training Area (Mockup)
This is the Maipo River basin,
where the model was trained.
where the model was trained.
Using a well documented landslide inventory,
we developed a robust prototype. This basin provides the foundation
for everything that comes next.
we developed a robust prototype. This basin provides the foundation
for everything that comes next.
Illustrative mockup of the SIBILA interface. The results shown correspond to preliminary tests under development and are for presentation purposes only.
Traditionally, generating a landslide susceptibility map in the Andes can take between eight to ten months.
With SIBILA, we are now achieving comparable results in less than one hour.
Pilot Area (Mockup)
Illustrative mockup of the SIBILA interface. The results shown correspond to preliminary tests under development and are for presentation purposes only.
The model demonstrated spatial consistency in the Yerba Loca basin,
establishing this territory as the first operational pilot
of a solution designed to scale internationally.
establishing this territory as the first operational pilot
of a solution designed to scale internationally.
Risk doesn't wait. Neither should we
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SIBILA
ANID
Financial Support
Universidad Mayor
Academic Collaboration
Dalia Marín Gálvez / [email protected] / +56950079414
Geomatics Engineer | M.Sc. in Remote Sensing / Geospatial Intelligence · GIS · Machine Learning · Applied AI
05:55
YouTube
Can We Predict Landslides? - SIBILA: Predictive Landslide System
SIBILA (Sistema Inteligente de Búsqueda e Identificación de Lugares Amenazados) es un sistema aplicativo de GeoAI para anticipar remociones en masa en territorios de montaña mediante integración avanzada de datos de Observación de la Tierra. Proyecto adjudicado por ANID (VIU) en colaboración con Universidad Mayor (UMayor) y Centro HÉMERA. Integra variables morfométricas, climáticas, geológicas e inventarios históricos para generar modelos predictivos transferibles entre cuencas. Actualmente en T