Articles | Volume 25, issue 11
https://doi.org/10.5194/nhess-25-4451-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-25-4451-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Community-driven natural hazard and physical vulnerability assessment in a disaster-prone urban neighborhood
Alejandro Builes-Jaramillo
CORRESPONDING AUTHOR
Institución Universitaria Colegio Mayor de Antioquia, Carrera 78 # 65-46 Bloque Fundacional piso 2, Medellín, Colombia
British Geological Survey, Nicker Hill, Keyworth, Nottingham, NG12 5GG, United Kingdom
British Geological Survey, Nicker Hill, Keyworth, Nottingham, NG12 5GG, United Kingdom
Nancy Quirós
Junta de Acción Comunal El Pacífico, Medellín, Colombia
Dairo Urán
Junta de Acción Comunal El Pacífico, Medellín, Colombia
James Rúa
Junta de Acción Comunal El Pacífico, Medellín, Colombia
Luis Alejandro Rivera-Flórez
CentroGeo, México City, México
Universidad de Antioquia, Medellín, Colombia
Institución Universitaria Colegio Mayor de Antioquia, Carrera 78 # 65-46 Bloque Fundacional piso 2, Medellín, Colombia
Camilo Restrepo-Estrada
Universidad de Antioquia, Medellín, Colombia
Ingry Natalia Gómez-Miranda
Institución Universitaria Pascual Bravo, Medellín, Colombia
Claire Dashwood
British Geological Survey, Nicker Hill, Keyworth, Nottingham, NG12 5GG, United Kingdom
João Porto de Albuquerque
School of Social and Political Sciences, University of Glasgow, Glasgow, United Kingdom
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Short summary
Short summary
On 2 April 2024, a Mw 7.4 earthquake hit Taiwan's eastern coast, causing extensive landslides and damage. We used automated methods combining Earth observation (EO) data with AI to quickly inventory the landslides. This approach identified 7090 landslides over 75 km2 within 3 h of acquiring the EO imagery. The study highlights AI's role in improving landslide detection efforts in disaster response.
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Short summary
This study addresses data gaps in urban neighborhoods often excluded from official hazard maps by co-developing a vulnerability assessment framework with community researchers in El Pacífico, Medellín. Through participatory methods, we created detailed building-scale hazard maps, improving community planning and advocacy for public interventions. This approach highlights how local knowledge can enhance risk assessments and support disaster risk reduction in marginalized areas.
This study addresses data gaps in urban neighborhoods often excluded from official hazard maps...
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