Cloud Type Distinction Based on CNN: An Aid for Short-Term Weather Forecast

被引：0

作者：

Martin-Abadal, Miguel ^{[1
]}

Gonzalez-Cid, Yolanda ^{[1
]}

Roig-Nomura, Joan ^{[1
]}

Jesus Manas, Jose ^{[2
]}

Nagy, Attila ^{[3
]}

Salom, Tomas ^{[3
]}

Alonso, Carlos ^{[3
]}

机构：

[1] Univ Balearic Isl, Dept Math & Comp Sci, Palma De Mallorca, Spain

[2] Wireless DNA SL, Palma De Mallorca, Spain

[3] Meteoclim Serv SL, Palma De Mallorca, Spain

来源：

ARTIFICIAL INTELLIGENCE RESEARCH AND DEVELOPMENT | 2019年 / 319卷

关键词：

deep learning; convolutional neural networks (CNN); semantic segmentation; pixel-wise cloud classification; meteorology;

D O I：

10.3233/FAIA190118

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Meteorology studies the behaviour of atmospheric phenomena in a specific place and time, it is of enormous dynamism and complexity. This paper presents an approach based on a deep semantic neural network that accurately performs a multiclass pixel-wise classification of diverse cloud types, providing information of great value to complement traditional weather assessment techniques. The obtained results suggest that simple web-cams can be of great help for observations that complement traditional data for short-term weather predictions and generations of warnings.

引用

页码：152 / 159

页数：8

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