Analyzing domain knowledge for big data analysis: A case study with urban tree type classification

Samantha Detor, Abigail Roh, Yiqun Xie, Shashi Shekhar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationBig Data Analytics - 7th International Conference, BDA 2019, Proceedings
EditorsSanjay Madria, Philippe Fournier-Viger, Sanjay Chaudhary, P. Krishna Reddy
PublisherSpringer
Pages176-192
Number of pages17
ISBN (Print)9783030371876
DOIs
StatePublished - Jan 1 2019
Event7th International Conference on Big Data Analytics, BDA 2019 - Ahmedabad, India
Duration: Dec 17 2019Dec 20 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11932 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference7th International Conference on Big Data Analytics, BDA 2019
CountryIndia
CityAhmedabad
Period12/17/1912/20/19

Keywords

  • Aerial imagery
  • Ground truthing
  • Labeled data creation
  • Smart cities
  • Tree disease
  • Urban tree big data
  • Urban trees

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  • Cite this

    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 S. Madria, P. Fournier-Viger, S. Chaudhary, & P. K. Reddy (Eds.), Big Data Analytics - 7th International Conference, BDA 2019, Proceedings (pp. 176-192). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11932 LNCS). Springer. https://doi.org/10.1007/978-3-030-37188-3_11