论文标题

具有客户端ML的移动公民科学应用程序的创作平台

Authoring Platform for Mobile Citizen Science Apps with Client-side ML

论文作者

Khan, Fahim Hasan, de Silva, Akila, Dusek, Gregory, Davis, James, Pang, Alex

论文摘要

数据收集是任何公民科学项目不可或缺的一部分。鉴于各种各样的项目,一定程度的专业知识,或者,对于新手参与者的某些指导可以大大提高收集到的数据的质量。公民科学项目的很大一部分取决于视觉数据,其中需要不同主题的照片或视频。通常,这些视觉数据是从世界各地(包括远程位置)收集的。在本文中,我们介绍了一个易于创建由客户端机器学习(ML)指导的公民科学项目的移动应用程序的创作平台。使用我们的平台创建的应用程序可以帮助参与者识别正确的数据并提高数据收集过程的效率。我们证明了我们提出的平台在两种用例中的应用:RIP当前检测应用程序用于计划的试点研究,以及用于生物多样性相关项目的检测应用程序。

Data collection is an integral part of any citizen science project. Given the wide variety of projects, some level of expertise or, alternatively, some guidance for novice participants can greatly improve the quality of the collected data. A significant portion of citizen science projects depends on visual data, where photos or videos of different subjects are needed. Often these visual data are collected from all over the world, including remote locations. In this article, we introduce an authoring platform for easily creating mobile apps for citizen science projects that are empowered with client-side machine learning (ML) guidance. The apps created with our platform can help participants recognize the correct data and increase the efficiency of the data collection process. We demonstrate the application of our proposed platform with two use cases: a rip current detection app for a planned pilot study and a detection app for biodiversity-related projects.

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