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Landslide Susceptibility Model by Means of Remote Sensing Images and AutoML
Cita
Renza Torres, Diego 
ORCID: https://orcid.org/0000-0001-8073-3594, Cárdenas, Elsa Adriana ORCID: https://orcid.org/0000-0002-8587-4625, Jaramillo, Carlos Marcelo, Weber, Serena Sarah ORCID: https://orcid.org/0000-0002-1268-7183 and Martínez Izquierdo, María Estíbaliz ORCID: https://orcid.org/0000-0003-0296-6151
(2021).
Landslide Susceptibility Model by Means of Remote Sensing Images and AutoML.
En: "Applied Computer Sciences in Engineering. WEA", October 2021, Medellín, Colombia. pp. 25-37.
https://doi.org/10.1007/978-3-030-86702-7_3.
ORCID: https://orcid.org/0000-0001-8073-3594, Cárdenas, Elsa Adriana ORCID: https://orcid.org/0000-0002-8587-4625, Jaramillo, Carlos Marcelo, Weber, Serena Sarah ORCID: https://orcid.org/0000-0002-1268-7183 and Martínez Izquierdo, María Estíbaliz ORCID: https://orcid.org/0000-0003-0296-6151
(2021).
Landslide Susceptibility Model by Means of Remote Sensing Images and AutoML.
En: "Applied Computer Sciences in Engineering. WEA", October 2021, Medellín, Colombia. pp. 25-37.
https://doi.org/10.1007/978-3-030-86702-7_3.
Descripción
| Título: | Landslide Susceptibility Model by Means of Remote Sensing Images and AutoML |
|---|---|
| Autor/es: |
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| Tipo de Documento: | Ponencia en Congreso o Jornada (Artículo) |
| Título del Evento: | Applied Computer Sciences in Engineering. WEA |
| Fechas del Evento: | October 2021 |
| Lugar del Evento: | Medellín, Colombia |
| Título del Libro: | Applied Computer Sciences in Engineering. WEA 2021. Communications in Computer and Information Science |
| Fecha: | 1 Enero 2021 |
| Volumen: | 1431 |
| Materias: | |
| ODS: | |
| Palabras Clave Informales: | Autokeras; AutoML; susceptibility; Autokeras; AutoML; LANDSLIDE; Susceptibility |
| Escuela: | E.T.S. de Ingenieros Informáticos (UPM) |
| Departamento: | Arquitectura y Tecnología de Sistemas Informáticos |
| Licencias Creative Commons: | Ninguna |
Resumen
Hydrometeorological phenomena, including mass movements, are a frequent threat that can generate a great impact at different levels. In order to estimate the susceptibility to mass movements, this work contains a new proposal to estimate the susceptibility to mass movements using a supervised learning algorithm designed using AutoML (Automated machine learning). Pixel-level information from Sentinel-2 multispectral images was used to train the model, and an expert’s susceptibility map was used as labels.
Más información
| ID de Registro: | 92861 |
|---|---|
| Identificador DC: | https://oa.upm.es/92861/ |
| Identificador OAI: | oai:oa.upm.es:92861 |
| URL Portal Científico: | https://portalcientifico.upm.es/es/ipublic/item/9350408 |
| Identificador DOI: | 10.1007/978-3-030-86702-7_3 |
| URL Oficial: | https://link.springer.com/chapter/10.1007/978-3-03... |
| Depositado por: | iMarina Portal Científico |
| Depositado el: | 14 Ene 2026 13:38 |
| Ultima Modificación: | 14 Ene 2026 13:38 |
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