Machine learning (ML) techniques excel at forecasting, clustering, and classification tasks, making them valuable for various aspects of mosquito control. In this literature review, we selected 120 papers relevant to the current state of ML for mosquito control in urban settings. The reviewed work covers several different methodologies, objectives, and evaluation criteria from various environmental contexts. We first divided the existing papers into geospatial, visual, or audio categories. For each category, we analyzed the machine learning pipeline, from dataset creation to model performance. We conclude with a discussion of the challenges and opportunities for further research. While the reviewed ML methods in mosquito control are promising, we recommend a) increased use of crowdsourced and citizen science data, b) a standardized and open ML pipeline for reproducible results, and c) research that incorporates advances in ML. With these suggestions, ML techniques could lead to effective mosquito control in urban environments.

机器学习（ML）技术擅长预测，聚类和分类任务，因此对于蚊子控制的各个方面都很有价值。在这篇文献综述中，我们选择了120篇与ML的现状有关的论文，用于城市环境中的蚊子控制。审查的工作涵盖了来自各种环境的几种不同的方法，目标和评估标准。我们首先将现有论文分为地理空间，视觉或音频类别。对于每个类别，我们分析了从数据集创建到模型性能的机器学习管道。最后，我们讨论了进一步研究的挑战和机遇。虽然经过审查的ML方法在蚊子控制中很有希望，但我们建议a）扩大使用众包和公民科学数据，b）标准化和开放的ML管道以确保可重现的结果，以及c）结合ML进步的研究。有了这些建议，机器学习技术可以在城市环境中有效控制蚊子。