Twenty-first century research is enabled by the availability of vast amounts of data, collected across a wide range of temporal and spatial scales, often in real time. Entire fleets of satellites, drones and other devices monitor the Earth at ever-increasing resolution, adding to the enormous corpus of maps and metadata that enable sciences from geology to biodiversity. While citizen science - or crowdsourcing science - has been successfully leveraged over the past several decades to close the analysis gap arising from large amounts of data, the sheer scale and complexity of these new data sets presents new challenges. This project addresses these challenges through novel Citizen Science Cyberinfrastructure (CSCI) where new tools and techniques, including teaming humans with Artificial Intelligence (AI), are being developed to enable researchers to efficiently extract the best results from large and complex data sets. This effort incorporates mapping, machine learning, and data sharing innovations in biodiversity, geoscience, and astronomy research and expands the capacity of research communities across a wide range of disciplines to use citizen science as a suitable, open and sustainable research methodology. This project leverages NSF-supported cyberinfrastructure, including the Zooniverse citizen science platform with its nearly three million volunteers, to provide a novel cyberinfrastructure by: (1) integrating mapping infrastructure into Zooniverse to accelerate accu