Sürdürülebilir Kalkınma Amaçları
Yayınlar
- BildiriAçık Erişim2021QUALITY ASSESSMENT of PANSHARPENED RASAT and LANDSAT-8 IMAGES USING SYNTHETIC SENTINEL-2 PANCHROMATIC IMAGE
Gunen, M.A.; Karas, I.R.; Ben Ahmed, M.; Boudhir, A.A.; Ane, B.K.
Technical and physical limitations often do not allow images to be acquired with high spatial and spectral resolution. Pansharpened images obtained by fusing high spatial resolution panchromatic images and multi-spectral images are widely used in GIS applications. In this study, it is aimed to increase the spatial resolution of the RASAT and Landsat-8 multispectral satellite images with synthetic Sentinel-2 panchromatic data. Six different pansharpening methods were used to test the success of the synthetic panchromatic data generation method using dataset with two different land use/land cover properties. Seven full reference image quality assessment metrics and two referenceless image quality assessment metrics were used to perform quantitative comparison.
- BildiriAçık Erişim2021GIS-BASED OPTIMUM SITE SELECTION for SOLAR ELECTRIC VEHICLE CHARGING STATION: ANKARA-ISTANBUL HIGHWAY CASE
Hisoglu, Seyit; Comert, R.; Karas, I.R.; Ben Ahmed, M.; Boudhir, A.A.; Ane, B.K.
Energy sources are divided into renewable and non-renewable sources. It can be seen that non-renewable energy resources are not adequately meeting the increased demands of worldwide technological developments, increasing population, and global consumption. Therefore, the demand for renewable resources is increasing day by day. When it comes to the use of non-renewable carbon-based fossil fuels, one of the first areas that come to mind is undoubtedly the Automotive sector. Today, it is realized that one of the main reasons for the lack of electric motor cars compared to petroleum fuelled cars, is the scarcity of electric vehicle charging stations and the difficulty of their accessibility. In this study; an analysis of solar-powered electric charging stations site selection was carried out for electric vehicles. The Ankara-Istanbul highway, which has a high traffic density, was chosen as the sample route for the study. Within the scope of the study, the areas where stations can be installed on the highway were carried out using the Multi-Criteria Decision Making Method with the help of Geographic Information System. Solar radiation, slope, aspect, land use/land cover, traffic volume and proximity to the road, criteria of the route, and site selection analysis were determined as input data. The maps of the determined criteria were arranged according to the study area and prepared for analysis. The criteria maps obtained were reclassified according to the above-mentioned criteria and scoring system. After the reclassification process, the weighting of each criteria which affect the analysis was determined by the researched literature and an overlapping process was carried out. According to the results map produced as a result of the overlay analysis, the appropriate area has been determined for the electric charging stations working with solar energy. On the defined route within the scope of the study, a proposal has been made for a total of 13 stations, 8 in Ankara, 3 in Bolu, 1 in Kocaeli, and 1 in Istanbul. This study, it is aimed to encourage automobile users to make greater use of electric motor vehicles, which would be a more environmentally friendly and sustainable choice, and ultimately more economical.
- BildiriAçık Erişim2017THE EFFICIENCY of RANDOM FOREST METHOD for SHORELINE EXTRACTION from LANDSAT-8 and GOKTURK-2 IMAGERIES
Bayram, B.; Erdem, F.; Akpinar, B.; Ince, A.K.; Bozkurt, S.; Catal Reis, H.; Seker, D.Z.; Karas, I.R.; Rahman, A.A.; Isikdag, U.
Coastal monitoring plays a vital role in environmental planning and hazard management related issues. Since shorelines are fundamental data for environment management, disaster management, coastal erosion studies, modelling of sediment transport and coastal morphodynamics, various techniques have been developed to extract shorelines. Random Forest is one of these techniques which is used in this study for shoreline extraction.. This algorithm is a machine learning method based on decision trees. Decision trees analyse classes of training data creates rules for classification. In this study, Terkos region has been chosen for the proposed method within the scope of "TUBITAK Project (Project No: 115Y718) titled "Integration of Unmanned Aerial Vehicles for Sustainable Coastal Zone Monitoring Model – Three-Dimensional Automatic Coastline Extraction and Analysis: Istanbul-Terkos Example". Random Forest algorithm has been implemented to extract the shoreline of the Black Sea where near the lake from LANDSAT-8 and GOKTURK-2 satellite imageries taken in 2015. The MATLAB environment was used for classification. To obtain land and water-body classes, the Random Forest method has been applied to NIR bands of LANDSAT-8 (5th band) and GOKTURK-2 (4th band) imageries. Each image has been digitized manually and shorelines obtained for accuracy assessment. According to accuracy assessment results, Random Forest method is efficient for both medium and high resolution images for shoreline extraction studies.