A TIMBER Framework for Mining Urban Tree Inventories Using Remote Sensing Datasets

Yiqun Xie, Han Bao, Shashi Shekhar, Joe Knight

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

10 Scopus citations

Abstract

Tree inventories are important datasets for many societal applications (e.g., urban planning). However, tree inventories still remain unavailable in most urban areas. We aim to automate tree identification at individual levels in urban areas at a large scale using remote sensing datasets. The problem is challenging due to the complexity of the landscape in urban scenarios and the lack of ground truth data. In related work, tree identification algorithms have mainly focused on controlled forest regions where the landscape is mostly homogeneous with trees, making the methods difficult to generalize to urban environments. We propose a TIMBER framework to find individual trees in complex urban environments and a Core Object REduction (CORE) algorithm to improve the computational efficiency of TIMBER. Experiments show that TIMBER can efficiently detect urban trees with high accuracy.

Original languageEnglish (US)
Title of host publication2018 IEEE International Conference on Data Mining, ICDM 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1344-1349
Number of pages6
ISBN (Electronic)9781538691588
DOIs
StatePublished - Dec 27 2018
Event18th IEEE International Conference on Data Mining, ICDM 2018 - Singapore, Singapore
Duration: Nov 17 2018Nov 20 2018

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
Volume2018-November
ISSN (Print)1550-4786

Conference

Conference18th IEEE International Conference on Data Mining, ICDM 2018
Country/TerritorySingapore
CitySingapore
Period11/17/1811/20/18

Bibliographical note

Funding Information:
ACKNOWLEDGMENT This material is based upon work supported by the National Science Foundation under Grants No. 1737633, 1541876, 1029711, IIS-1320580, 0940818 and IIS-1218168, the US-DOD under Grants HM0210-13-1-0005, ARPA-E under Grant No. DE-AR0000795, USDA under Grant No. 2017-51181-27222, NIH under Grant No. UL1 TR002494, KL2 TR002492 and TL1 TR002493 and the OVPR U-Spatial and Minnesota Supercomputing Institute at the University of Minnesota. We also thank Kim Koffolt for improving the paper’s readability.

Funding Information:
This material is based upon work supported by the National Science Foundation under Grants No. 1737633, 1541876, 1029711, IIS-1320580, 0940818 and IIS-1218168, the USDOD under Grants HM0210-13-1-0005, ARPA-E under Grant No. DE-AR0000795, USDA under Grant No. 2017-51181- 27222, NIH under Grant No. UL1 TR002494, KL2 TR002492 and TL1 TR002493 and the OVPR U-Spatial and Minnesota Supercomputing Institute at the University of Minnesota. We also thank Kim Koffolt for improving the paper's readability

Publisher Copyright:
© 2018 IEEE.

Keywords

  • Remote sensing
  • TIMBER
  • Tree detection
  • Urban

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