Cited by 121Automated Road and Building Extraction3D Modeling in Geospatial ApplicationsRemote Sensing and LiDAR Applications

Building type classification using spatial and landscape attributes derived from LiDAR remote sensing data

Lu, Zhenyu, Im, Jungho, Rhee, Jinyoung, Hodgson, Michael·2014·Landscape and Urban Planning

Integrity signals

Registry

Crossref · 10.1016/j.landurbplan.2014.07.005

Cites

64 of 64 references verified0 citation errors · 0 not checkable

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  • LiDAR data are often used to retrieve heights or classify buildings at city scale, but its collection is expensive.

    “To generate 3D models, LiDAR data are often used to retrieve heights or classify buildings at city scale [339,496], but its collection is expensive.”

    From Tackling climate change with machine learning (2023) · cites this paper as [496]

  • Remote sensing is key to inferring infrastructure data, as satellite data are globally available and largely consistent.

    “Remote sensing is key to inferring infrastructure data [79,224,339,496,535,868] as satellite data 19 present a source of information that is globally available and largely consistent worldwide.”

    From Tackling climate change with machine learning (2023) · cites this paper as [496]

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Authors
Lu, Z., Im, J., Rhee, J., & Hodgson, M.
Affiliations
  • SUNY College of Environmental Science and Forestry (US)
  • APEC Climate Center (KR)
  • University of South Carolina (US)
Corresponding author
Im, Jungho
Venue
Landscape and Urban Planning
Publisher
Elsevier BV
Published
2014-10
Volume
130
Pages
134-148
ISSN
0169-2046 (ISSN-L)
OpenAlex
W2137825465
Cited by
121
References
64
Citations by year
2026: 9 · 2025: 12 · 2024: 14 · 2023: 9 · 2022: 10 · 2021: 12 · 2020: 10 · 2019: 12 · 2018: 7 · 2017: 14 · 2016: 7 · 2015: 3 · 2014: 2
FWCI
11.53
Type
journal article
Language
en
Open access
closed
Locations
  • Landscape and Urban Planning (publishedVersion)
  • Scholarworks@UNIST (Ulsan National Institute of Science and Technology) (submittedVersion)
Topics
  • Physical Sciences › Engineering › Ocean Engineering › Automated Road and Building Extraction
  • Physical Sciences › Engineering › Building and Construction › 3D Modeling in Geospatial Applications
  • Physical Sciences › Environmental Science › Environmental Engineering › Remote Sensing and LiDAR Applications
Keywords
building type classification, LiDAR, spatial characteristics, landscape attributes, single-family house, non-residential buildings, building geometry, footprint area, urban planning, Denver

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