Progressing towards Estimates of Local Emissions from Trees in Cities: A Transdisciplinary Framework Integrating Available Municipal Data, AI, and Citizen Science
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Published:2023-12-31
Issue:1
Volume:14
Page:396
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ISSN:2076-3417
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Container-title:Applied Sciences
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language:en
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Short-container-title:Applied Sciences
Author:
Mayer Julia1, Memmel Martin1, Ruf Johannes1, Patel Dhruv1, Hoff Lena2, Henninger Sascha2
Affiliation:
1. German Research Center for Artificial Intelligence (DFKI) GmbH, Trippstadter Straße 112, 67663 Kaiserslautern, Germany 2. Department of Spatial and Environmental Planning, Physical Geography and Didactics, University of Kaiserslautern-Landau, Pfaffenbergstraße 95, 67663 Kaiserslautern, Germany
Abstract
Urban tree cadastres, crucial for climate adaptation and urban planning, face challenges in maintaining accuracy and completeness. A transdisciplinary approach in Kaiserslautern, Germany, complements existing incomplete tree data with additional precise GPS locations of urban trees. Deep learning models using aerial imagery identify trees, while other applications employ street view imagery and LIDAR data to collect additional attributes, such as height and crown width. A web application encourages citizen participation in adding features like species and improving datasets for further model training. The initiative aims to minimize resource-intensive maintenance conducted by local administrations, integrate additional features, and improve data quality. Its primary goal is to create transferable AI models utilizing aerial imagery and LIDAR data that can be applied in regions with similar tree populations. The approach includes tree clusters and private trees, which are essential for assessing allergy and ozone potential but are usually not recorded in municipal tree cadastres. The paper highlights the potential of improving tree cadastres for effective urban planning in a transdisciplinary approach, taking into account climate change, health, and public engagement.
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference22 articles.
1. Albert, L., Fröhlich, N., Hausbrand, N., Henninger, S., Maurer, T., and Ruzika, S. (2022). Mobility, Knowledge and Innovation Hubs in Urban and Regional Development, Proceedings of the REAL CORP 2022, 27th International Conference on Urban Development, Regional Planning and Information Society, Vienna, Austria, 14–16 November 2022, CORP—Competence Center of Urban and Regional Planning. 2. Rauch, S., Morrison, G., Norra, S., and Schleicher, N. (2014). Urban Environment-Proceedings of the 11th Urban Environment Symposium (UES), Karlsruhe, Germany, 16–19 September 2012, Springer. 3. Metz, M., Weinmann, A., and Krisztian, L. (2023, January 15–18). Automatisierte Detektion von Baumstandorten in der Metropole Ruhr. Proceedings of the Tagungsband FOSSGIS-Konferenz, Berlin, Germany. 4. Ventura, J., Pawlak, C., Honsberger, M., Gonsalves, C., Rice, J., Love, N.L.R., Han, S., Nguyen, V., Sugano, K., and Doremus, J. (2022). Individual Tree Detection in Large-Scale Urban Environments using High-Resolution Multispectral Imagery. arXiv. 5. Weinstein, B.G., Marconi, S., Bohlman, S., Zare, A., and White, E. (2019). Individual Tree-Crown Detection in RGB Imagery Using Semi-Supervised Deep Learning Neural Networks. Remote Sens., 11.
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