TY  - JOUR
TI  - AIRBORNE LIDAR DATA CLASSIFICATION IN COMPLEX URBAN AREA USING RANDOM FOREST: A CASE STUDY OF BERGAMA, TURKEY
AB  - Airborne Light Detection and Ranging (LiDAR) data have been increasingly used for classification of urbanareas in the last decades. Classification of urban areas is especially crucial to separate the area into classes for urbanplanning, mapping, and change detection monitoring purposes. In this study, an airborne LiDAR data of a complex urbanarea from Bergama District, İzmir, Turkey were classified into four classes; buildings, trees, asphalt road, and ground.Random Forest (RF) supervised classification method is selected as classification algorithm and pixel-wise classificationwas performed. Ground truth of the area was generated by digitizing classes into features to select training data and tovalidate the results. The selected study area from Bergama district is complex in urban planning of buildings, road, andground. The buildings are very close to each other, and trees are also very close to buildings and sometimes cover therooftops of buildings. The most challenging part of this study is to generate ground truth in such a complex area. Accordingto the obtained classification results, the overall accuracy of the results is found as 70,20%. The experimental results showedthat the algorithm promises reliable results to classify airborne LiDAR data into classes in a complex urban area.
AU  - CANAZ SEVGEN, SİBEL
DO  - 10.26833/ijeg.440828
PY  - 2019
JO  - International Journal of Engineering and Geosciences
VL  - 4
IS  - 1
SN  - 2548-0960
SP  - 45
EP  - 51
DB  - TRDizin
UR  - http://search/yayin/detay/334382
ER  -