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Rotation-blended CNNs on a New Open Dataset for Tropical Cyclone Image-to-intensity Regression

Published: 19 July 2018 Publication History

Abstract

Tropical cyclone (TC) is a type of severe weather systems that occur in tropical regions. Accurate estimation of TC intensity is crucial for disaster management. Moreover, the intensity estimation task is the key to understand and forecast the behavior of TCs better. Recently, the task has begun to attract attention from not only meteorologists but also data scientists. Nevertheless, it is hard to stimulate joint research between both types of scholars without a benchmark dataset to work on together. In this work, we release a such a benchmark dataset, which is a new open dataset collected from satellite remote sensing, for the TC-image-to-intensity estimation task. We also propose a novel model to solve this task based on the convolutional neural network (CNN). We discover that the usual CNN, which is mature for object recognition, requires several modifications when being used for the intensity estimation task. Furthermore, we combine the domain knowledge of meteorologists, such as the rotation-invariance of TCs, into our model design to reach better performance. Experimental results on the released benchmark dataset verify that the proposed model is among the most accurate models that can be used for TC intensity estimation, while being relatively more stable across all situations. The results demonstrate the potential of applying data science for meteorology study.

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  1. Rotation-blended CNNs on a New Open Dataset for Tropical Cyclone Image-to-intensity Regression

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    KDD '18: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
    July 2018
    2925 pages
    ISBN:9781450355520
    DOI:10.1145/3219819
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 19 July 2018

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    Author Tags

    1. atmospheric science
    2. blending
    3. convolutional neural network
    4. dropout
    5. pooling
    6. regression
    7. tropical cyclone
    8. tropical cyclone intensity

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    • Ministry of Science and Technology of Taiwan

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    KDD '18 Paper Acceptance Rate 107 of 983 submissions, 11%;
    Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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    • (2025)Tropical cyclone intensity estimation based on YOLO-NAS using satellite images in real timeAlexandria Engineering Journal10.1016/j.aej.2024.10.072113(227-241)Online publication date: Mar-2025
    • (2024)MT-GN: Multi-Task-Learning-Based Graph Residual Network for Tropical Cyclone Intensity EstimationRemote Sensing10.3390/rs1602021516:2(215)Online publication date: 5-Jan-2024
    • (2024)Spatiotemporal fusion convolutional neural network: tropical cyclone intensity estimation from multisource remote sensing imagesJournal of Applied Remote Sensing10.1117/1.JRS.18.01850118:01Online publication date: 1-Jan-2024
    • (2024)Physics-Informed Learning for Tropical Cyclone Intensity PredictionIEEE Transactions on Geoscience and Remote Sensing10.1109/TGRS.2024.350662762(1-21)Online publication date: 2024
    • (2024)FHDTIE: Fine-Grained Heterogeneous Data Fusion for Tropical Cyclone Intensity EstimationIEEE Transactions on Geoscience and Remote Sensing10.1109/TGRS.2024.348967462(1-15)Online publication date: 2024
    • (2024)TCIP-Net: Quantifying Radial Structure Evolution for Tropical Cyclone Intensity PredictionIEEE Transactions on Geoscience and Remote Sensing10.1109/TGRS.2024.345071162(1-14)Online publication date: 2024
    • (2024)Rapid Weakening Tropical Cyclone Intensity Estimation Based on Deep Learning Using Infrared Satellite Images and Reanalysis DataIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing10.1109/JSTARS.2024.346582917(17598-17611)Online publication date: 2024
    • (2024)Deep learning fusion of multi-channel satellite images improves the accuracy of tropical cyclone intensity estimation2024 16th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC)10.1109/IHMSC62065.2024.00035(124-127)Online publication date: 24-Aug-2024
    • (2024)Physical Structure Representation and Environmental Data Fusion for Cyclone Intensity PredictionIGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium10.1109/IGARSS53475.2024.10640887(7316-7319)Online publication date: 7-Jul-2024
    • (2024)Monitoring tropical cyclone using multi-source data and deep learning: a reviewInternational Journal of Image and Data Fusion10.1080/19479832.2024.2411677(1-21)Online publication date: 8-Oct-2024
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