Linda See
Senior Research Scholar
Novel Data Ecosystems for Sustainability Research Group
Advancing Systems Analysis Program
Biography
Linda See has a PhD in spatial applications of fuzzy logic from the School of Geography, University of Leeds, where she taught for more than a decade as a senior lecturer in Computational Geography and GIS. She has an MSc and BSc in physical geography and environmental management from McMaster University and the University of Toronto. In between her MSc and PhD, she spent one year working at the Max Planck Institute for Atmospheric Sciences near Goettingen, Germany, followed by four years at the Food and Agriculture Organization (FAO) of the United Nations in Rome, Italy, on agrometeorology and early warning for food security.Dr. See's research interests include artificial intelligence-based methods, geographic information systems (GIS), land cover, crowdsourcing and citizen science. As part of the Novel Data Ecosystems for Sustainability (NODES) group in the Advancing Systems Analysis (ASA) program, she works with the Geo-Wiki team on crowdsourcing of land cover, quality assurance of crowdsourced data, and community building. She has coordinated the Austrian ASAP-10 LACO-Wiki project, which is an online tool for the validation of land cover and the Austrian-funded project ADAPT-UHI, which has developed strategies for climate change mitigation and adaptation in small to medium-sized cities in Austria. She has supported the H2020-funded LandSense Citizen Observatory, in particular the pilot studies involving the French National Mapping Agency (IGN) and BirdLife International, and is a member of three Communities of Practice in the H2020 WeObserve project. She is an editor of the journal Environment and Planning B: Urban Analytics and City Science.
Last update: 26 JAN 2021
Publications
See, L. , Sturn, T., Karanam, S., Subash, A., McCallum, I. , & Fritz, S. (2022). Combining a rapid image classification app with artificial intelligence through the Picture Pile Platform for citizen science. In: ECSA Conference, 5-8 October 2022, Berlin.
Fritz, S. , See, L. , & Grey, F. (2022). The grand challenges facing environmental citizen science. Frontiers in Environmental Science 10 e1019628. 10.3389/fenvs.2022.1019628.
Hadi, H., Yowargana, P., Zulkarnain, M.T., Mohamad, F., Goib, B.K., Hultera, P., Sturn, T., Karner, M., Dürauer, M., See, L. , Fritz, S. , Hendriatna, A., Nursafingi, Afi, Melati, D.N., Prasetya, F.V.A.S., Carolita, I., Kiswanto, Firdaus, M.I., Rosidi, M., & Kraxner, F. (2022). A national-scale land cover reference dataset from local crowdsourcing initiatives in Indonesia. Scientific Data 9 (1) 10.1038/s41597-022-01689-5.
Proden, E., Bett, K., Chen, H., Duerto Valero, S., Fraisl, D. , Gamez, G., MacFeely, S., Mondardini, R., See, L. , & Min, Y. (2022). Citizen science data to track SDG progress: Low-hanging fruit for Governments and National Statistical Offices. Crowd4SDG, Policy Brief, July 2022
Moltchanova, E., Lesiv, M. , See, L. , Mugford, J., & Fritz, S. (2022). Optimizing Crowdsourced Land Use and Land Cover Data Collection: A Two-Stage Approach. Land 11 (7) e958. 10.3390/land11070958.