YOLO Convolutional neural network for building damage detection in hydrometeorological disasters using satellite imagery
Natural disasters pose a continuous threat to populations worldwide, with hydrometeorological disasters standing out due to their unpredictability, rapid onset, and significant destructive capacity. Consequently, countries invest substantial resources in implementing pre- and post-disaster measures,...
- Autores:
-
Moreno González, César Luis
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2024
- Institución:
- Universidad de los Andes
- Repositorio:
- Séneca: repositorio Uniandes
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.uniandes.edu.co:1992/74680
- Acceso en línea:
- https://hdl.handle.net/1992/74680
- Palabra clave:
- Machine Learning
Deep Learning
Computer Vision
Detection Models
Natural Disasters
Hydrometeorological Disasters
Ingeniería
- Rights
- embargoedAccess
- License
- https://repositorio.uniandes.edu.co/static/pdf/aceptacion_uso_es.pdf