Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System, Science, Tsinghua University, Beijing 100084, China.
Department of Geography, University of Brasilia (UnB), Brasilia CEP 70910-900, Brazil.
Int J Environ Res Public Health. 2020 Jun 23;17(12):4509. doi: 10.3390/ijerph17124509.
In recent years there has been an increasing use of satellite Earth observation (EO) data in dengue research, in particular the identification of landscape factors affecting dengue transmission. Summarizing landscape factors and satellite EO data sources, and making the information public are helpful for guiding future research and improving health decision-making. In this case, a review of the literature would appear to be an appropriate tool. However, this is not an easy-to-use tool. The review process mainly includes defining the topic, searching, screening at both title/abstract and full-text levels and data extraction that needs consistent knowledge from experts and is time-consuming and labor intensive. In this context, this study integrates the review process, text scoring, active learning (AL) mechanism, and bidirectional long short-term memory (BiLSTM) networks, and proposes a semi-supervised text classification framework that enables the efficient and accurate selection of the relevant articles. Specifically, text scoring and BiLSTM-based active learning were used to replace the title/abstract screening and full-text screening, respectively, which greatly reduces the human workload. In this study, 101 relevant articles were selected from 4 bibliographic databases, and a catalogue of essential dengue landscape factors was identified and divided into four categories: land use (LU), land cover (LC), topography and continuous land surface features. Moreover, various satellite EO sensors and products used for identifying landscape factors were tabulated. Finally, possible future directions of applying satellite EO data in dengue research in terms of landscape patterns, satellite sensors and deep learning were proposed. The proposed semi-supervised text classification framework was successfully applied in research evidence synthesis that could be easily applied to other topics, particularly in an interdisciplinary context.
近年来，卫星对地观测（EO）数据在登革热研究中得到了越来越多的应用，特别是在识别影响登革热传播的景观因素方面。总结景观因素和卫星 EO 数据源，并公开这些信息有助于指导未来的研究和改善卫生决策。在这种情况下，文献综述似乎是一种合适的工具。然而，这并不是一种易于使用的工具。综述过程主要包括定义主题、搜索、在标题/摘要和全文层面进行筛选以及数据提取，这些都需要专家的一致知识，并且耗时费力。在这种情况下，本研究集成了综述过程、文本评分、主动学习（AL）机制和双向长短期记忆（BiLSTM）网络，并提出了一种半监督文本分类框架，能够高效、准确地选择相关文章。具体来说，文本评分和基于 BiLSTM 的主动学习分别用于替代标题/摘要筛选和全文筛选，这大大减少了人工工作量。本研究从 4 个文献数据库中选择了 101 篇相关文章，并确定和分为四类：土地利用（LU）、土地覆盖（LC）、地形和连续地表特征。此外，还列出了用于识别景观因素的各种卫星 EO 传感器和产品。最后，提出了在景观格局、卫星传感器和深度学习方面应用卫星 EO 数据在登革热研究中的可能未来方向。所提出的半监督文本分类框架成功地应用于研究证据综合，可以很容易地应用于其他主题，特别是在跨学科背景下。
