Abstract
The goals of this research were to create a labeled dataset of tree shadows and to test the feasibility of shadow-based tree type identification using aerial imagery. Urban tree big data that provides information about individual trees can help city planners optimize positive benefits of urban trees (e.g., increasing wellbeing of city residents) while managing potential negative impacts (e.g., risk to power lines). The continual rise of tree type specific threats, such as emerald ash borer, due to climate change has made this problem more pressing in recent years. However, urban tree big data are time consuming to create. This paper evaluates the potential of a new tree type identification method that utilizes shadows in aerial imagery to survey larger regions of land in a shorter amount of time. This work is challenging because there are structural variations across a given tree type and few verified tree type identification datasets exist. Related work has not explored how tree structure characteristics translate into a profile view of a tree’s shadow or quantified the feasibility of shadow-only based tree type identification. We created a consistent and accurate dataset of 4,613 tree shadows using ground truthing procedures and novel methods for ensuring consistent collection of spatial shadow data that take binary and spatial agreement between raters into account. Our results show that identifying trees from shadows in aerial imagery is feasible and merits further exploration in the future.
S. Detor and A. Roh—These authors contributed equally to this work.
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Acknowledgements
This study is supported by the US NSF under Grants No. 1901099, 1737633, 1541876, 1029711, IIS-1320580, 0940818 and IIS-1218168, the USDOD under Grants HM0210-13-1-0005, USDA under Grant No. 2017-51181-27222, ARPA-E under Grant No. DE-AR0000795, NIH under Grant No. UL1 TR002494, KL2 TR002492 and TL1 TR0024-93, and the OVPR U-Spatial and Minnesota Supercomputing Institute at the University of Minnesota. Aerial imagery used in this work and shown in this paper was supplied by Hennepin and Ramsey County. Field surveys were provided by the City of St. Paul Forestry Unit and the University of Minnesota. We thank Kim Koffolt for improving the readability of this paper. We also thank the Spatial Computing Group for their feedback throughout this work.
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Detor, S., Roh, A., Xie, Y., Shekhar, S. (2019). Analyzing Domain Knowledge for Big Data Analysis: A Case Study with Urban Tree Type Classification. In: Madria, S., Fournier-Viger, P., Chaudhary, S., Reddy, P. (eds) Big Data Analytics. BDA 2019. Lecture Notes in Computer Science(), vol 11932. Springer, Cham. https://doi.org/10.1007/978-3-030-37188-3_11
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