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

Landscape and Urban Planning

Volumn 130, Issue 1, 2014, Pages 134-148

  Lu, Zhenyu ^a   Im, Jungho ^b,c   Rhee, Jinyoung ^d   Hodgson, Michael ^e  

^a Anchor QEA LLC   (United States)

^b Ulsan National Institute of Science and Technology   (South Korea)

^c 402 Baker Laboratory   (United States)

^d APEC Climate Center   (South Korea)

^e UNIVERSITY OF SOUTH CAROLINA   (United States)

Author keywords

Building classification; Decision trees; LiDAR; Machine learning; Random forest; Support vector machines

Indexed keywords

BUILDINGS; CLASSIFICATION (OF INFORMATION); DECISION TREES; LEARNING SYSTEMS; RANDOM FORESTS; REMOTE SENSING; SUPPORT VECTOR MACHINES; OPTICAL RADAR;

CLASSIFICATION RESULTS; LIDAR REMOTE SENSING; LIGHT DETECTION AND RANGING; MANAGEMENT PRACTICES; NEIGHBORHOOD ENVIRONMENT; RESIDENTIAL BUILDING; SURROUNDING BUILDINGS; SURROUNDING ENVIRONMENT;

OPTICAL RADAR; BUILDINGS;

BUILDING; FOREST; LAND CLASSIFICATION; LANDSCAPE; LIDAR; NEIGHBORHOOD; PARKING; REMOTE SENSING; ROAD; URBAN PLANNING; CLASSIFICATION; MACHINERY; MANAGEMENT PRACTICE; SATELLITE DATA;

COLORADO; DENVER; UNITED STATES;

EID: 84906080423     PISSN: 01692046     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.landurbplan.2014.07.005     Document Type: Article

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