Collecting whole sky images and classification of cloud genera and conditions

• MORIKAWA Yu

  Kobe Digital Labo Inc.

• NAKANISHI Haru

  Kobe Digital Labo Inc.

• INAMURA Naoki

  BANYAN PARTNERS Inc.

• KONDO Nobuaki

  BANYAN PARTNERS Inc.

• OBUCHI Hiroki

  SKY Perfect JSAT Corporation

• OHSAWA Teruo

  Graduate School of Maritime Sciences, Kobe University

• MATSUBARA Takashi

  Graduate School of System Informatics, Kobe University

• UEHARA Kuniaki

  Graduate School of System Informatics, Kobe University

Bibliographic Information

Other Title

• 全天球画像のデータ収集と雲形と状態判定

Abstract

Maritime meteorological observation is critical for a safe voyage, and general ships are required in Japan to report the observations to parties concerned. Since it is difficult to recognize the meteorological conditions for non-experts, the demand of automatic recognition arises. Many studies have tackled the classification of cloud genera and the regression of cloud cover. However, less attention has been paid for cloud conditions. Thus, we developed a machine learning system for classification of cloud conditions. We first developed a dedicated equipment for photographing whole sky images and collected data samples. Then, we tagged cloud genera and conditions in each cloud layer (high, middle, and low). Using the dataset, we built a deep convolutional neural network to classify the cloud genera and conditions via fine-tuning ResNet50. The network achieved accuracies higher than 0.9 for both cloud genera and conditions.

Journal

• Proceedings of the Annual Conference of JSAI

  Proceedings of the Annual Conference of JSAI JSAI2018 (0), 2A401-2A401, 2018

  The Japanese Society for Artificial Intelligence

Details

• CRID: 1390001288048963840
• NII Article ID: 130007422223
• DOI: 10.11517/pjsai.jsai2018.0_2a401
• Text Lang: ja
• Data Source:
  • JaLC
  • CiNii Articles
• Abstract License Flag: Disallowed