GlobeNet: Convolutional Neural Networks for Typhoon Eye Tracking from Remote Sensing Imagery

Hong, Seungkyun; Kim, Seongchan; Joh, Minsu; Song, Shiji

doi:10.48550/arxiv.1708.03417

Cited by 13 publications

(12 citation statements)

References 2 publications

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“…They contain those 76 typhoons from 1993 till 2017 that hit or were about to hit the Korean peninsula. Whereas Hong et al (2017) 8 used satellite images directly, in this study preprocessing of the images is inevitable. During the 25 years of capture time, different satellites have been operating.…”

Section: Datasetmentioning

confidence: 99%

“…The network successfully detected the shape of a cyclone and predicted the future movement direction. Hong et al (2017) 8 utilized multi-layer neural networks to predict the position of the cyclone's eye in a single high-resolution 3D remote sensing image. The network learns the coordinates of the eye from labeled images of past data and predicts them in test images.…”

Section: Introductionmentioning

confidence: 99%

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Typhoon track prediction using satellite images in a Generative Adversarial Network

Rüttgers¹,

Lee²,

You³

2018

Preprint

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Tracks of typhoons are predicted using satellite images as input for a Generative Adversarial Network (GAN). The satellite images have time gaps of 6 hours and are marked with a red square at the location of the typhoon center. The GAN uses images from the past to generate an image one time step ahead. The generated image shows the future location of the typhoon center, as well as the future cloud structures. The errors between predicted and real typhoon centers are measured quantitatively in kilometers. 42.4% of all typhoon center predictions have absolute errors of less than 80 km, 32.1% lie within a range of 80 -120 km and the remaining 25.5% have accuracies above 120 km. The relative error sets the above mentioned absolute error in relation to the distance that has been traveled by a typhoon over the past 6 hours. High relative errors are found in three types of situations, when a typhoon moves on the open sea far away from land, when a typhoon changes its course suddenly and when a typhoon is about to hit the mainland. The cloud structure prediction is evaluated qualitatively. It is shown that the GAN is able to predict trends in cloud motion. In order to improve both, the typhoon center and cloud motion prediction, the present study suggests to add information about the sea surface temperature, surface pressure and velocity fields to the input data.

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Section: Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Typhoon track prediction using satellite images in a Generative Adversarial Network

Rüttgers¹,

Lee²,

You³

2018

Preprint

View full text Add to dashboard Cite

show abstract

“…The network successfully detected the shape of a cyclone and predicted the future movement direction. Hong et al 4 utilized multi-layer neural networks to predict the position of the cyclone's eye in a single high-resolution 3D remote sensing image. The network learns the coordinates of the eye from labeled images of past data and predicts them in test images.…”

Section: Introductionmentioning

confidence: 99%

Prediction of a typhoon track using a generative adversarial network and satellite images

Rüttgers

Lee

Jeon

et al. 2019

Sci Rep

114

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Tracks of typhoons are predicted using a generative adversarial network (GAN) with satellite images as inputs. Time series of satellite images of typhoons which occurred in the Korea Peninsula in the past are used to train the neural network. The trained GAN is employed to produce a 6-hour-advance track of a typhoon for which the GAN was not trained. The predicted track image of a typhoon favorably identifies the future location of the typhoon center as well as the deformed cloud structures. Errors between predicted and real typhoon centers are measured quantitatively in kilometers. An averaged error of 95.6 km is achieved for tested 10 typhoons. Predicting sudden changes of the track in westward or northward directions is identified as a challenging task, while the prediction is significantly improved, when velocity fields are employed along with satellite images.

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“…In order to benchmark HPC AI systems, the first step is to figure out how DL works in scientific fields. Although it is an emerging field, several scientific fields have applied DL to solve many important problems, such as extreme weather analysis [38,39,40,19], high energy physics [34,35,36,37,20], and cosmology [21,24,31,32,33].…”

Section: Deep Learning In Scientific Computingmentioning

confidence: 99%

“…Racah et al (2017) [40] implement a multichannel spatiotemporal CNN architecture for semi-supervised prediction and exploratory extreme weather data analysis. GlobeNet [39] is a CNN model with inception units for typhoon eye tracking. Kurth et al (2018) [19] use variants of Tiramisu and DeepLabv3+ neural networks which are both built on Residual Network (ResNet) [18].…”

Section: Extreme Weather Analysismentioning

confidence: 99%

HPC AI500: A Benchmark Suite for HPC AI Systems

Jiang

Gao

Wang

et al. 2019

Benchmarking, Measuring, and Optimizing

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In recent years, with the trend of applying deep learning (DL) in high performance scientific computing, the unique characteristics of emerging DL workloads in HPC raise great challenges in designing, implementing HPC AI systems. The community needs a new yard stick for evaluating the future HPC systems. In this paper, we propose HPC AI500 -a benchmark suite for evaluating HPC systems that running scientific DL workloads. Covering the most representative scientific fields, each workload from HPC AI500 is based on real-world scientific DL applications. Currently, we choose 14 scientific DL benchmarks from perspectives of application scenarios, data sets, and software stack. We propose a set of metrics for comprehensively evaluating the HPC AI systems, considering both accuracy, performance as well as power and cost. We provide a scalable reference implementation of HPC AI500. The specification and source code are publicly available from http://www.benchcouncil.org/HPCAI500/index.html. Meanwhile, the AI benchmark suites for datacenter, IoT, Edge are also released on the BenchCouncil web site.

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GlobeNet: Convolutional Neural Networks for Typhoon Eye Tracking from Remote Sensing Imagery

Cited by 13 publications

References 2 publications

Typhoon track prediction using satellite images in a Generative Adversarial Network

Typhoon track prediction using satellite images in a Generative Adversarial Network

Prediction of a typhoon track using a generative adversarial network and satellite images

HPC AI500: A Benchmark Suite for HPC AI Systems

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