Automatic detection of single street trees from airborne LiDAR data based on point segmentation methods
INTERNATIONAL JOURNAL OF ENGINEERING AND GEOSCIENCES, cilt.8, sa.2, ss.129-137, 2023 (ESCI, TRDizin)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 8 Sayı: 2
- Basım Tarihi: 2023
- Doi Numarası: 10.26833/ijeg.1079210
- Dergi Adı: INTERNATIONAL JOURNAL OF ENGINEERING AND GEOSCIENCES
- Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), Central & Eastern European Academic Source (CEEAS), Directory of Open Access Journals, TR DİZİN (ULAKBİM)
- Sayfa Sayıları: ss.129-137
- Yıldız Teknik Üniversitesi Adresli: Evet
Özet
As a primary element of urban ecosystem, street trees are very essential for environmental
quality and aesthetic beauty of urban landscape. Street trees play a crucial role in everyday life
of city inhabitants and therefore, comprehensive and accurate inventory information for street
trees is required. In this research, an automatic method is proposed to detect single street
trees from airborne Light Detection and Ranging (LiDAR) point cloud instead of traditional
field work or photo interpretation. Firstly, raw LiDAR point cloud data have been classified to
obtain high vegetation class with a hierarchical rule-based classification method. Then, the
LiDAR points in high vegetation class were segmented with mean shift and Density Based
Spatial Clustering of Applications with Noise (DBSCAN) algorithms to acquire single urban
street trees in the Davutpasa Campus of Yildiz Technical University, Istanbul, Turkey. The
accuracy assessment of the acquired street trees was also conducted using completeness and
correctness analyses. The acquired results from urban study area approved the success of the
proposed point-based approach for automatic detection of single street trees using LiDAR
point cloud.