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Yayın A theoretical comparison of ResNet and DenseNet architectures on the subject of shoreline extraction(Işık Üniversitesi, 2020-09-23) Ecevit, Mert İlhan; Çavdaroğlu, Gülsüm Çiğdem; Işık Üniversitesi, Fen Bilimleri Enstitüsü, Enformasyon Teknolojileri Yüksek Lisans ProgramıToday's Deep Learning technologies provides numerous approaches on the subject of convolutional networks. These approaches serve researchers to train datasets and generate wanted results from these datasets. Each CNN architecture has its own strong points and weak sides. Because of this situation a comparison between these architectures is a valuable asset. Image processing is a method that is frequently used to process remotely sensed data in remote sensing studies.. Between current architectures, RESNET and DENSENET architectures are chosen to be used by Dr. Çavdaroğlu for her project on TÜBİTAK. The result of this comparison will be used in that project in order to apply most ecient architecture. This thesis is written to draw outlines of RESNET and DENSENET and create a foresight for further projects which can be supported by this thesis. In order to achieve an accurate image recognition process in remote sensing domain, a preliminary research is requisite. As a research thesis this work serves the purpose of learning manner of works, performance indicators of RESNET and DENSENET convolutional networks. The result of this research will create a baseline for an academical project. At the other hand, comparison of these two convolutional network approaches provides information to decide which approach is more suitable for remote sensing projects depending upon the subject of the project. For future works on Remote Sensing this thesis work will serve a guideline and reason for preference. The presented thesis work has been developed as the technical feasibility of the 3501 TÜBITAK Project named "Uydu Görüntülerinden Kıyı Sınırlarının Derin Öğrenme Yöntemleri ile Otomatik Çıkarımı", applied by Dr. G. Çiğdem Çavdaroğlu, and the thesis results will be applied within the scope of the Project after the project acceptance.Yayın Spatial-Temporary analysis of Istanbul air pollution during the pandemic using Google Earth Engine and Google community mobility reports(Gök, Murat, 2023-06-30) Çavdaroğlu, Gülsüm Çiğdem; Arık, Ahmet OkanThe Covid-19 pandemic has brought drastic changes to people's daily life and environmental characteristics. To control the pandemic, all governments have implemented particular policies for their countries and imposed restrictions that affect people's daily life. The traffic index has decreased in many countries and cities depending on the restrictions. Therefore, restrictions in many countries and cities have positively impacted air quality. However, the opposite has also been observed in metropolitan cities. In this study, the change in the air quality of Istanbul, which is accepted as Turkey's largest metropolitan city, has been examined. First, the spatio-temporal distribution of air pollutants (NO2, CO, and SO2) has been analyzed using Sentinel-5P NRTI satellite images. Then six independent variable groups (traffic index of Istanbul, daily deaths in Istanbul, Google community mobility reports of Istanbul, fuel prices, stringency index of Turkey, two logical attributes regarding the Covid-19 restrictions and in-class education) were collected and combined to analyze the correlations between these variable groups and air pollutant concentrations. According to the spatial distribution graphs, there is a tendency to decrease NO2, CO, and SO2 pollutant concentrations in Istanbul when the restrictions are applied in Turkey. There was no significant relationship between the decrease in community mobility in Istanbul and pollutant concentrations, although an increase in air quality has been observed in many cities due to the restrictions of the Covid-19 pandemic.
