Revolutionizing tree management via intelligent spatial techniques

Yiqun Xie, Shashi Shekhar, Richard Feiock, Joseph Knight

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

2 Scopus citations

Abstract

Tree management is becoming a big issue in a variety of societal domains. In recent years, historic wildfires and blackouts caused by failures in tree management have increased in both quantity and severity, resulting in many deaths and financial loses in the tens of billions of dollars. Many communities are also suffering from massive tree loss (e.g., in the millions) that affects the health and well-being of citizens. These problems are likely to worsen due to climate change, aging infrastructure and population growth. Tree management needs a revolution to deal with these urgent problems. This opens up new challenges and opportunities for the spatial community. This paper presents some of the open research problems from the perspectives of individual tree mapping and characterization as well as decision making and in-field intervention.

Original languageEnglish (US)
Title of host publication27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2019
EditorsFarnoush Banaei-Kashani, Goce Trajcevski, Ralf Hartmut Guting, Lars Kulik, Shawn Newsam
PublisherAssociation for Computing Machinery
Pages71-74
Number of pages4
ISBN (Electronic)9781450369091
DOIs
StatePublished - Nov 5 2019
Event27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2019 - Chicago, United States
Duration: Nov 5 2019Nov 8 2019

Publication series

NameGIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems

Conference

Conference27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2019
CountryUnited States
CityChicago
Period11/5/1911/8/19

Bibliographical note

Funding Information:
This work 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. We also thank Sam Detor and Abigail Roh for their help on data generation and evaluation.*%blankline%*

Funding Information:
This work 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. We also thank Sam Detor and Abigail Roh for their help on data generation and evaluation.

Publisher Copyright:
© 2019 Copyright held by the owner/author(s).

Keywords

  • Intelligent techniques
  • Spatial
  • Tree management
  • Vision

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