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

Yiqun Xie; Han Bao; Shashi Shekhar; Joe Knight

doi:10.1109/ICDM.2018.00183

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 proceeding › Conference contribution

19 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 language	English (US)
Title of host publication	2018 IEEE International Conference on Data Mining, ICDM 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1344-1349
Number of pages	6
ISBN (Electronic)	9781538691588
DOIs	https://doi.org/10.1109/ICDM.2018.00183
State	Published - Dec 27 2018
Event	18th IEEE International Conference on Data Mining, ICDM 2018 - Singapore, Singapore Duration: Nov 17 2018 → Nov 20 2018

Publication series

Name	Proceedings - IEEE International Conference on Data Mining, ICDM
Volume	2018-November
ISSN (Print)	1550-4786

Conference

Conference	18th IEEE International Conference on Data Mining, ICDM 2018
Country/Territory	Singapore
City	Singapore
Period	11/17/18 → 11/20/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities

Keywords

Remote sensing
TIMBER
Tree detection
Urban

Publisher link

10.1109/ICDM.2018.00183

Find It

Find It at U of M Libraries

Cite this

Xie, Y., Bao, H., Shekhar, S., & Knight, J. (2018). A TIMBER Framework for Mining Urban Tree Inventories Using Remote Sensing Datasets. In 2018 IEEE International Conference on Data Mining, ICDM 2018 (pp. 1344-1349). Article 8594992 (Proceedings - IEEE International Conference on Data Mining, ICDM; Vol. 2018-November). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDM.2018.00183

A TIMBER Framework for Mining Urban Tree Inventories Using Remote Sensing Datasets. / Xie, Yiqun; Bao, Han; Shekhar, Shashi et al.
2018 IEEE International Conference on Data Mining, ICDM 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1344-1349 8594992 (Proceedings - IEEE International Conference on Data Mining, ICDM; Vol. 2018-November).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Xie, Y, Bao, H, Shekhar, S & Knight, J 2018, A TIMBER Framework for Mining Urban Tree Inventories Using Remote Sensing Datasets. in 2018 IEEE International Conference on Data Mining, ICDM 2018., 8594992, Proceedings - IEEE International Conference on Data Mining, ICDM, vol. 2018-November, Institute of Electrical and Electronics Engineers Inc., pp. 1344-1349, 18th IEEE International Conference on Data Mining, ICDM 2018, Singapore, Singapore, 11/17/18. https://doi.org/10.1109/ICDM.2018.00183

@inproceedings{683facfd495b4c26833bd249e0fa648c,

title = "A TIMBER Framework for Mining Urban Tree Inventories Using Remote Sensing Datasets",

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.",

keywords = "Remote sensing, TIMBER, Tree detection, Urban",

author = "Yiqun Xie and Han Bao and Shashi Shekhar and Joe Knight",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 18th IEEE International Conference on Data Mining, ICDM 2018 ; Conference date: 17-11-2018 Through 20-11-2018",

year = "2018",

month = dec,

day = "27",

doi = "10.1109/ICDM.2018.00183",

language = "English (US)",

series = "Proceedings - IEEE International Conference on Data Mining, ICDM",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1344--1349",

booktitle = "2018 IEEE International Conference on Data Mining, ICDM 2018",

}

TY - GEN

T1 - A TIMBER Framework for Mining Urban Tree Inventories Using Remote Sensing Datasets

AU - Xie, Yiqun

AU - Bao, Han

AU - Shekhar, Shashi

AU - Knight, Joe

PY - 2018/12/27

Y1 - 2018/12/27

N2 - 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.

AB - 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.

KW - Remote sensing

KW - TIMBER

KW - Tree detection

KW - Urban

UR - https://www.scopus.com/pages/publications/85061356737

UR - https://www.scopus.com/pages/publications/85061356737#tab=citedBy

U2 - 10.1109/ICDM.2018.00183

DO - 10.1109/ICDM.2018.00183

M3 - Conference contribution

AN - SCOPUS:85061356737

T3 - Proceedings - IEEE International Conference on Data Mining, ICDM

SP - 1344

EP - 1349

BT - 2018 IEEE International Conference on Data Mining, ICDM 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 18th IEEE International Conference on Data Mining, ICDM 2018

Y2 - 17 November 2018 through 20 November 2018

ER -

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

Abstract

Publication series

Conference

Bibliographical note

UN SDGs

Keywords

Publisher link

Find It

Other files and links

Fingerprint

Cite this