Challenges and implications of predicting the spatiotemporal distribution of dengue fever outbreak in Chinese Taiwan using remote sensing data and deep learning-Reference-Cited by-同舟云学术

Challenges and implications of predicting the spatiotemporal distribution of dengue fever outbreak in Chinese Taiwan using remote sensing data and deep learning

Published:2023-01-17 Issue: Volume: Page:1-7
ISSN:1009-5020
Container-title:Geo-spatial Information Science
language:en
Short-container-title:Geo-spatial Information Science

Author:

Anno Sumiko¹^ORCID,Tsubasa Hirakawa²^ORCID,Sugita Satoru²^ORCID,Yasumoto Shinya²^ORCID,Lee Ming-An³^ORCID,Sasaki Yoshinobu⁴^ORCID,Oyoshi Kei⁴^ORCID

Affiliation:

1. Graduate School of Global Environmental Studies, Sophia University, Tokyo, Japan

2. Chubu Institute for Advanced Studies, Chubu University, Aichi, Japan

3. Centre of Excellence for Ocean Engineering and Department of Environmental Biology and Fisheries Science, National Taiwan Ocean University, Keelung, China

4. Earth Observation Research Center, Japan Aerospace Exploration Agency, Ibaraki, Japan

Funder

Japan Aerospace Exploration Agency

Collaboration Research Program of IDEAS, Chubu University

Publisher

Informa UK Limited

Subject

Computers in Earth Sciences,Geography, Planning and Development

Link

https://www.tandfonline.com/doi/pdf/10.1080/10095020.2022.2144770

Reference28 articles.

1. Convolutional neural network for automated mass segmentation in mammography

2. Spatiotemporal dengue fever hotspots associated with climatic factors in Taiwan including outbreak predictions based on machine-learning

3. Towards Operational Satellite-Based Damage-Mapping Using U-Net Convolutional Network: A Case Study of 2011 Tohoku Earthquake-Tsunami

4. Impacts of El Niño Southern Oscillation and Indian Ocean Dipole on dengue incidence in Bangladesh

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