Authoring Platform for Mobile Citizen Science Apps with Client-side ML
| Autor: | Fahim Hasan Khan, Akila de Silva, James Davis, Alex Pang, Gregory Dusek |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
Data collection business.industry Computer science media_common.quotation_subject Mobile apps Client-side Crowdsourcing Variety (cybernetics) World Wide Web Computer Science - Computers and Society Computers and Society (cs.CY) Citizen science Use case Quality (business) business media_common |
| Zdroj: | CSCW Companion |
| DOI: | 10.48550/arxiv.2212.05411 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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