Sürdürülebilir Kalkınma Amaçları
Yayınlar
- MakaleYayıncıda açık erişim2022Analyzing and Predicting Spatiotemporal Urban Sprawl in Eskişehir Using Remote Sensing Data
Küçük Matcı, Dilek; Çömert, Resul; Avdan, Uğur
Urban areas of major cities in developing countries are expanding rapidly due to rapid population growth and industrialization. Determining the reasons for rapid urbanization and the amount of urban area that will be needed in the future is important for the planned growth of these cities. In this study, the LU/LC change of Eskisehir city center that has a rapid rate of urbanization and industrialization in Turkey between 1984 and 2020 was determined. Then, the area needed for urban and industrial areas in 2030 was investigated using the Cullullar Automata–Markov Chain (CA–MC) hybrid model simulation. Each year's LU/LC map was produced with over 80% kappa accuracies performing the pixel-based Random Forest (RF) algorithm for change analysis. In the change analysis made between 1984 and 2020, it was determined that there was a change of 117% in the urban area and 977% in the industrial area. The prediction was made for the validation of the CA–MC model from the period 2000–2010 to 2020. When compared with the 2020 LU/LC map, the model success was obtained as 0.84, 0.87, and 0.87 in the Kstandard, Klocation, and Kno Kappa metrics, respectively. By using the 2010–2020 periods in the estimation of 2030, it has been observed that the urban and industrial area will increase by 22.95%; therefore, there will be decreases in agriculture and other natural areas as in previous years.
- MakaleAçık Erişim2018Detection of collapsed building from unmanned aerial vehicle data with object based image classification
Avdan, Uğur; Çömert, Resul; Küçük Matcı, Dilek
Buildings are most affected the objects by earthquake disaster. Detection of collapsed buildings after an earthquake isimportant both for determining the current situation and quick response. Unmanned aerial vehicles that have evolved inrecent years, can provide very high resolution images of the earth surface using camera systems attached to them.Information for the intended purpose can be obtained through the products produced from these images.In this study, collapsed buildings were detected in the area where high-resolution images were obtained whit unmanned aerialvehicle in 2015 and 2014. Building detection process was made based on a scenario events. In this context, 2015 images weretaken before the earthquake and 2014 images were taken after the earthquake. The images of both years were processedseparately to produce the digital elevation model and orthophoto image of the study area. building of the study area wereobtained by applying the object-based classification process to the generated data. 11 buildings which were available in thearea in 2015 and not available in the area in 2014, were detected successfully comparison of building classes of two years.