Presentation information

Oral presentation

General Session » [General Session] 2. Machine Learning

[2A4] [General Session] 2. Machine Learning

Wed. Jun 6, 2018 5:20 PM - 7:00 PM Room A (4F Emerald Hall)

座長:椿 真史(産業技術総合研究所)

5:20 PM - 5:40 PM

[2A4-01] Collecting whole sky images and classification of cloud genera and conditions

〇Yu Morikawa2, Haru Nakanishi2, Naoki Inamura1, Nobuaki Kondo1, Hiroki Obuchi3, Teruo Ohsawa5, Takashi Matsubara4, Kuniaki Uehara4 (1. BANYAN PARTNERS Inc., 2. Kobe Digital Labo Inc., 3. SKY Perfect JSAT Corporation, 4. Graduate School of System Informatics, Kobe University, 5. Graduate School of Maritime Sciences, Kobe University)

Keywords: AI, Deeplearning, Whole sky images, Classification of cloud genera and conditions, Maritime meteorological observation

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.