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Good practice Imported

Mincyt's AI Damage-Mapping Tool — Venezuela Fuses Multimodal Models With Before/After Satellite Imagery After the June 2026 La Guaira Earthquakes

Venezuela · Caracas · See the Venezuela profile · See the Caracas profile

Evidence: Descriptive / self-reported Top 89% 33/100 · Ask Evidence Copilot about this practice

After the June 24, 2026 earthquakes struck La Guaira, Venezuela's science ministry built an AI tool combining multimodal models with before/after satellite and drone imagery to auto-classify damage; ABAE processed 1,400+ images into 100+ mapping products for emergency responders.

1,400+
Satellite images processed (June 2026)
100+
Mapping products generated (June 2026)
Mincyt's AI Damage-Mapping Tool — Venezuela Fuses Multimodal Models With Before/After Satellite Imagery After the June 2026 La Guaira Earthquakes

Details

Maturity
Pilot
Promoter
Ministerio del Poder Popular para Ciencia y Tecnología (Mincyt) / Agencia Bolivariana para Actividades Espaciales (ABAE)
Period
June 2026–present
Keywords
disaster response, emergency management, geospatial analysis

Context

After the 24 June 2026 La Guaira earthquakes, Venezuela's Mincyt and space agency ABAE built an AI tool fusing multimodal models with before/after satellite and drone imagery to auto-classify structural damage.

Results

ABAE processed 1,400+ satellite images into 100+ mapping products delivered to Civil Protection, the armed forces and a presidential commission; figures are self-reported, with no independent audit of classification accuracy and unclear differentiation from parallel NASA/Microsoft/EU efforts using the same feeds.

Implementation
Indicative cost
Low (< €50k)
Time to results
Short (< 1 year)
Staffing & skills
Mincyt AI directorate, ABAE (Agencia Bolivariana para Actividades Espaciales)

Conditions for success

  • access to International Charter Space and Major Disasters imagery
  • coordination with parallel international damage-mapping efforts

Common failure modes

  • no independent audit of AI classification accuracy
  • unclear how much of the classification work is distinctly homegrown versus parallel international efforts

Commonly funded by

National / regional programmes

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Data sources

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