Connected Cyberinfrastructure: New Crowdsourced Mapping Infrastructure for GIS through Zooniverse
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# Connected Cyberinfrastructure: New Crowdsourced Mapping Infrastructure for GIS through Zooniverse
## Abstract
Zooniverse.org is the world's largest platform for people-powered research, connecting millions of volunteers with professional research teams to analyze complex datasets. More than 1 billion volunteer classifications across astronomy, ecology, biology, and other disciplines have enabled discoveries, datasets, and publications that would not otherwise be possible. More than 500 projects have launched through Zooniverse's free DIY Project Builder (zooniverse.org/lab).
A recent NSF-funded effort is extending Zooniverse’s infrastructure to support crowdsourced mapping and geospatial data processing. The new mapping tools enable volunteers and machine-learning systems to work together to accurately process, curate, and georeference complex datasets, creating new possibilities for geoscience, biodiversity, and disaster response.
Demonstrator projects illustrate the range of applications. In Beaver Dams, volunteers outline dams in satellite imagery, creating training data for AI models that identify dams and quantify their ecological contributions. In Notes from Nature, volunteers review machine-generated coordinates for historical plant specimens, transforming museum records into mappable biodiversity datasets. In McMurdo Dry Valleys, volunteers georeference historical Antarctic aerial photographs by identifying features shared with modern imagery, while machine-learning tools refine locations. Most recently, the Zooniverse Planetary Response Network launched a new project in response to flooding in Nepal, with volunteers identifying blocked roads and damaged bridges in satellite imagery to support humanitarian response efforts.
Together, these projects demonstrate a new model of connected cyberinfrastructure in which people and machines combine their complementary strengths to transform complex geospatial data into actionable, research-ready datasets at scale.