Automated detection of burned areas in Costa Rica: a first approach
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| Autores: | , , , , |
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| Formato: | artículo original |
| Estado: | Versión publicada |
| Data de Publicación: | 2026 |
| Descripción: | In Costa Rica despite diverse studies carried out by wildfires, collection data still is arduous fieldwork due to geographical conditions, there are zones where accessibility conditions prevent data collections. Satellite images are tools useful to study different zones to detect burned areas or their scars, but processing data by researchers requires too much time due to the number of files that need to be analyzed. We propose in this paper a framework based on machine learning and spectral index analysis to help burned area detection with efficient computational performance. Selecting as our study area in the Guanacaste Conservation Area, we obtained data from Sentinel-2 mission; we could detect the most probable zones affected by wildfire. Although this is a first step in the prevention of wildfire in protected zones, our results demonstrate the potential to develop a future robust detecting system. |
| País: | Portal de Revistas TEC |
| Institución: | Instituto Tecnológico de Costa Rica |
| Repositorio: | Portal de Revistas TEC |
| Idioma: | Español |
| OAI Identifier: | oai:ojs.pkp.sfu.ca:article/8504 |
| Acceso en liña: | https://revistas.tec.ac.cr/index.php/tec_marcha/article/view/8504 |
| Palabra crave: | áreas quemadas detección clasificación aprendizaje automático aplicaciones prácticas de IA rendimiento computacional Burned areas detection classification machine learning practical applications of AI computational performance |