Akademik Veri Yönetim Sistemi
International Journal of Engineering and Geosciences, cilt.8, sa.1, 2023 (ESCI)
The need for accurate and up-to-date spatial data by decision-makers in
public and private administrations is increasing gradually. In recent decades,
in the management of disasters and smart cities, fast and accurate extraction
of roads, especially in emergencies, is quite important in terms of transportation,
logistics planning, and route determination. In this study, automatic road extraction analyses were carried out
using the Unmanned Aerial Vehicle (UAV) data set,
belonging to the Yildiz Technical University Davutpasa Campus road route.
For this purpose, this paper presents a comparison between performance analysis
of rule-based classification and U-Net deep learning method for solving
automatic road extraction problems. Objects belonging to the road
and road network were obtained with the rule-based classification method with
overall accuracy of 95%, and with the deep learning method with an overall accuracy of
86%. On the other hand, the performance metrics including accuracy,
recall, precision, and F1 score were utilized to evaluate the performance analysis
of the two methods. These values were obtained from
confusion matrices for 4 target classes consisting of road and road elements
namely road, road line, sidewalk, and bicycle road. Finally, integration of
classified image objects with ontology was realized. Ontology was developed by
defining four target class results obtained as a result of the rule-based
classification method, conceptual class definition and properties, rules, and
axioms.