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Semi-Supervised Text Classification Framework: An Overview of Dengue Landscape Factors and Satellite Earth

Zhichao Li1, Helen Gurgel2,3, Nadine Dessay3,4

  • 1Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System, Science, Tsinghua University, Beijing 100084, China.

International Journal of Environmental Research and Public Health
|June 27, 2020
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Summary

This study introduces a semi-supervised text classification framework using active learning and BiLSTM networks to efficiently identify landscape factors in dengue research from satellite Earth observation data.

Keywords:
deep active learningdenguelandscapenatural language processingsatellite Earth observation

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Area of Science:

  • Environmental Science
  • Public Health
  • Geospatial Science

Background:

  • Satellite Earth observation (EO) data is increasingly used in dengue research to identify transmission-influencing landscape factors.
  • Traditional literature reviews are time-consuming and labor-intensive for synthesizing this complex information.
  • A need exists for efficient methods to summarize landscape factors and EO data sources for better health decision-making.

Purpose of the Study:

  • To propose and validate a semi-supervised text classification framework for efficient and accurate selection of relevant articles in dengue research.
  • To integrate text scoring, active learning (AL), and bidirectional long short-term memory (BiLSTM) networks to streamline the literature review process.
  • To identify and categorize essential dengue landscape factors and associated satellite EO data.

Main Methods:

  • Developed a semi-supervised text classification framework combining text scoring and BiLSTM-based active learning.
  • Used the framework to screen articles, replacing manual title/abstract and full-text review stages.
  • Selected 101 relevant articles from four bibliographic databases.

Main Results:

  • Identified and cataloged essential dengue landscape factors into four categories: land use (LU), land cover (LC), topography, and continuous land surface features.
  • Tabulated various satellite EO sensors and products utilized for identifying these landscape factors.
  • Successfully applied the framework in research evidence synthesis, demonstrating its efficiency and accuracy.

Conclusions:

  • The proposed semi-supervised text classification framework significantly reduces human workload in literature synthesis for dengue research.
  • The identified landscape factors and EO data provide valuable guidance for future research and health policy.
  • The framework is adaptable for interdisciplinary research evidence synthesis beyond dengue studies.