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Using Expert Crowdsourcing to Annotate Extreme Weather Events

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Abstract

The harsh impacts of extreme weather events like cyclones or precipitation extremes are increasingly being felt with hazardous consequences. These extreme events are exceptions to well-known weather patterns and therefore are not forecasted with current automatic computational methods. In this context, the use of human computation to annotate extreme atmospheric phenomena could provide novel insights for computational forecasting algorithms and a step forward in climate change research by enabling the early detection of abnormal weather conditions. However, existing crowd computing solutions have technological limitations and show several gaps when involving expert crowds. This paper presents a research approach to fulfill some of the technological and knowledge gaps for expert crowds’ participation. A case study on expert annotation of extreme atmospheric phenomena is used as a baseline for an innovative architecture able to support expert crowdsourcing. The full stack service-oriented architecture ensures interoperability and provides an end-to-end approach able to fetch weather data from international databases, generating experts’ visualizations (weather maps), annotating data by expert crowds, and delivering annotated data for processing weather forecasts. An implementation of the architecture suggests that it can deliver an effective mechanism for expert crowd work while solving some of the identified issues with extant platforms. Therefore, we conclude that the proposed architecture has the potential to contribute as an effective annotation solution for extreme weather events.

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Notes

  1. 1.

    https://www.ecmwf.int/.

  2. 2.

    https://www.djangoproject.com.

  3. 3.

    https://matplotlib.org.

  4. 4.

    https://reactjs.org.

  5. 5.

    https://www.ecmwf.int/en/forecasts/access-forecasts/ecmwf-web-api.

  6. 6.

    https://pybossa.com.

  7. 7.

    https://leafletjs.com.

  8. 8.

    https://cds.climate.copernicus.eu.

  9. 9.

    https://www.windy.com.

  10. 10.

    https://gis.ncdc.noaa.gov/maps/ncei.

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Correspondence to Dennis Paulino .

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Paulino, D., Correia, A., Barroso, J., Liberato, M., Paredes, H. (2021). Using Expert Crowdsourcing to Annotate Extreme Weather Events. In: Rocha, Á., Adeli, H., Dzemyda, G., Moreira, F., Ramalho Correia, A.M. (eds) Trends and Applications in Information Systems and Technologies . WorldCIST 2021. Advances in Intelligent Systems and Computing, vol 1366. Springer, Cham. https://doi.org/10.1007/978-3-030-72651-5_50

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