Attention to Fires: Multi-Channel Deep Learning Models for Wildfire Severity Prediction
Wildfires are one of the natural hazards that the European Union is actively monitoring through the Copernicus EMS Earth observation program which continuously releases public information related to such catastrophic events. Such occurrences are the cause of both short- and long-term damages. Thus,...
Guardado en:
Autores principales: | Simone Monaco, Salvatore Greco, Alessandro Farasin, Luca Colomba, Daniele Apiletti, Paolo Garza, Tania Cerquitelli, Elena Baralis |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
MDPI AG
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/ab07aa6e9ce145faab961e7300fb4986 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Field observations and remote sensing techniques for evaluation of vegetal recovery after different wildfire severity in a Mediterranean ecosystem
por: Francos,Marcos, et al.
Publicado: (2021) -
An Attention-Based Convolutional Neural Network for Acute Lymphoblastic Leukemia Classification
por: Muhammad Zakir Ullah, et al.
Publicado: (2021) -
S2A: Scale-Attention-Aware Networks for Video Super-Resolution
por: Taian Guo, et al.
Publicado: (2021) -
Tactical Decision-Making for Autonomous Driving Using Dueling Double Deep Q Network With Double Attention
por: Shuwei Zhang, et al.
Publicado: (2021) -
A Knowledge Graph-Enhanced Attention Aggregation Network for Making Recommendations
por: Dehai Zhang, et al.
Publicado: (2021)