Browsing by Author "Kemec, S."

Now showing 1 - 3 of 3

The Effect of Spatial Accessibility and Visibility on the Spatial Configuration and Users in Shopping Center Design: The Case of Van Shopping Center
(Nova Science Publishers Inc., 2025) Bayram, S.; Kemec, S.; Yeler, O.
Spaces today are designed and expanded to meet human needs, with various methods employed by architectural disciplines during the planning and design process. One such method, Space Syntax, uses mapping to numerically analyze spatial orientation, formation, and accessibility on a human scale. This approach is particularly beneficial for complex buildings like museums, airports, and hospitals, where wayfinding and space alignment are crucial. Testing designs with Space Syntax before construction ensures more user-friendly interiors. Shopping centers, as examples of complex structures, often feature standardized designs. The Space Syntax method can map current usage patterns to optimize navigation, store layouts, sizes, and visibility. This study, conducted in Van, eastern Turkiye, used Space Syntax to analyze accessibility and visibility at the Van Shopping Center, a site with high user density. © 2025 by Nova Science Publishers, Inc.
An Evaluation of Land Use Land Cover (Lulc) Classification for Urban Applications With Quickbird and Worldview2 Data
(Institute of Electrical and Electronics Engineers Inc., 2015) Cavur, M.; Kemec, S.; Nabdel, L.; Sebnem Duzgun, H.
Monitoring and analysis of the land and rapid environmental change, leads to the use of Land Use and Land Cover (LULC) classification approaches from remote sensing data. The main focus of this aper is to illustrate the practical approach to analysis and mapping of land use and land cover features using high resolution satellite images. The study is carried out for two different places, Basel and Tel Aviv. For this purpose, Quickbird satellite imagery is used for Basel and WorldView2 imagery for Tel Aviv. The classification method chosen for the Quickbird image is Support Vector Machine (SVM) classifier and Maximum Likelihood method for the WordView2 satellite imagery. Both of the methods are applied using ENVI 5.0 Remote Sensing software. An accuracy assessment is also applied to the classified results based on the ground truth points or known reference pixels. © 2015 IEEE.
Land Use and Land Cover Classification of Sentinel 2-A: St Petersburg Case Study
(International Society for Photogrammetry and Remote Sensing, 2019) Cavur, M.; Duzgun, H.S.; Kemec, S.; Demirkan, D.C.
Land use and land cover (LULC) maps in many areas have been used by companies, government offices, municipalities, and ministries. Accurate classification for LULC using remotely sensed data requires State of Art classification methods. The SNAP free software and ArcGIS Desktop were used for analysis and report. In this study, the optical Sentinel-2 images were used. In order to analyze the data, an object-oriented method was applied: Supported Vector Machines (SVM). An accuracy assessment is also applied to the classified results based on the ground truth points or known reference pixels. The overall classification accuracy of 83,64% with the kappa value of 0.802 was achieved using SVM. The study indicated that of SVM algorithms, the proposed framework on Sentinel-2 imagery results is satisfactory for LULC maps. © Authors 2019.