In the study, the temporal change analysis of Akkaya Reservoir in Niğde was made. The Reservoir was established in 1964. The change of reservoir between 1999, 2009 and 2019 was analyzed using remote sensing method. Satellite images used in the study belong to August (1999, 2009, 2019). MNDWI (Modified Normalized Difference Water Index) analysis was used. With the results of this analysis; the amount of water surfaces in the reservoir basin was calculated by years. After this analysis, NDVI (Normalized Difference Vegetation Index) analysis was used and land cover types were determined by CORINE. It has been determined that the water surface map of 2009 covers a larger area compared to other years. When only the area where the reservoir is located is evaluated by the polynomial equation (2nd degree), there may be a decrease of approximately 26% in the reservoir water surface area within 15 years. As a result of the study, it has been determined that Landsat satellite data can be used to determine the water surface amounts. If study in smaller areas will be done, use the lower resolution satellite images are suggested. In this study, MNDWI analysis was found more successful than NDVI analysis to find water surface area. Multi-band satellite images provide important qualitative information in evaluating the functions used in ecosystem-based planning studies.

This is study is based with the support of RS-GIS technology on the land use of Samsun Center, as well as the coastal districts of Ilkadım,Atakum,Bafra Plain, through the processing and interpretation of satellite images in the summer months of 2000,2010,2020. Spatial and temporal variability properties of LU/LC were determined using MLC algorithm, controlled classification approach. The predictive values of the LU/LC change that will occur in 2030, calculated with the MLP‑ANN model based on Machine Learning algorithms and mapped with the QGIS 3.16 program. To determine the accuracy coefficient of the model, 2020 LU/LC simulation performed using the transition potential matrix of 2000 and 2010 LU/LC data. The results of simulation were compared the data of land use land cover with the 2020 to evaluate the accuracy of the simulation model. The model of MLP‑ANN provided an accuracy of 72% based on the kappa fit index. According to MLP‑ANN model 2030 results were an increase of 73.33 km² in built up areas, an increase of 56.89 km² in bare areas, and a decrease of 129.66 km² in green areas. It provided a reference basis for future Samsun urban to rural coastline LU planning and management and LU structure optimization.

The average precipitation on the irrigation field can be estimated from the Meteorology Observation Stations by using spatial interpolation methods such as Thiessen polygon and isohyetal curves. However, the fact that precipitation doesn't occur homogenous in spatial scales, spatial interpolation methodologies need a large number of meteorology stations for more accurate results. In recent years, remote sensing methods have diversified to estimate precipitation. In this study, performance of the satellite-based precipitation data was assessed to determine areal precipitation over an irrigation area. This study was conducted over left bank irrigation area located in the Çukurova Plain of Turkey. Relationship between CHIRPS satellite based on monthly precipitation data and 4 meteorology stations’ data were analyzed. Determination coefficients (R2) of the stations were found between 0.64 and 0.77, for point based comparison, R2 was calculated as 0.84 with Thiessen polygon method. It is concluded that the precipitation amount in the irrigated area can be estimated as accurately as classical methods such as Thiessen polygon with satellite-based precipitation data.

Family health centers in Turkey started to be implemented for the first time in Düzce in 2004 years within the scope of Law No. 5258. While determining the physical conditions of the places where family health centers are built, the first item in the regulation is that the building should be easily accessible. This situation shows the importance of the subject in terms of accessibility. While determining the features of the places where FHCs will be made, environmental characteristics are also taken into consideration. Environmental features are effective in determining the FHCs location in different ways. These impacts are divided into two groups: the physical features that pavements, roads and parks can include, and the social, cultural and institutional features of neighborhoods that include local social ties and collective activities. From this point of view, the importance of the location of family health centers relative to roads and houses is understood. The aim of this study is to examine the accessibility of Family Health Centers in Konyaaltı, Antalya, on a neighborhood basis using Geographic Information Systems. Konyaaltı has 21 Family Health Centers. As a result of the analyses, it was determined that most of the neighborhoods had problems in terms of accessibility, while a very few of them did not experience problems in terms of accessibility. In terms of the total number of buildings, the ratio of buildings that are 500 meters walking distance from any family health center by using highways is 35.56%. With these rates, 3,634 of the 10,2018 buildings remain within the limits of the regulation. Finally; suggestions were made to increase accessibility to these areas.

Urban green spaces are areas established to meet the recreational needs of urban people. Although green spaces vary from country to country and region in terms of plan and design features, they were basically created to allow people to meet with nature. Parks are the basic components of urban landscapes that provide environmental and social functional value. Urban parks, in particular, provide spaces for outdoor physical activities. In order to take advantage of the opportunities of activities in the parks, users must have convenient access to these resources. One of the most important aspects for researching the use and potential benefits of urban green spaces is the assessment of their geographic accessibility. The widespread use of smart city systems and the gradual expansion of their usage areas increase the importance of spatial analysis. Spatial analyses are used in today’s urban management in the processes of determining social needs, identifying current problems, and putting forward solutions. When spatial analyses are used together with GIS, the field of application develops even more, and it supports local governments in responding to the changing demands of the society for a better life. In the study, the adequacy and accessibility of 160 city parks in Konyaaltı District of Antalya Province were examined. In terms of the adequacy of the parks, the area value of 10 m2 per person determined with the Construction Plan numbered 3194 was taken as basis. In terms of accessibility, distance values of 200, 400, 800, 1,200 meters were examined. Neighborhood boundaries and population information were obtained from the relevant units, and Arc-GIS software was used in the analysis. It was determined that the parks in Konyaaltı district were insufficient in terms of adequacy and accessibility. Finally, suggestions were made in terms of increasing the adequacy of the parks and ensuring accessibility.

Developments in Geographic Information Systems and Remote Sensing (RS) technologies and innovative approaches emerging in deep learning (DL) supported analysis methods have an important place in disaster research as in every field. Convolutional neural networks such as Mask RCNN, U-NET, one of the deep learning methods for disaster damage impact assessment and classification, have started to show successful results. However, high-resolution geospatial imagery and drones provide faster and more accurate detection of structural damage.  In this study, damaged building detection was performed using Göktürk-1 satellite images from 6 February 2023 using Mask-RCNN architecture. In this study, deep learning methods were used to detect the collapsed buildings in the city of Malatya during the 6 February 2023 earthquakes. The study aims to emphasize the significance of GIS and remote sensing for the timely and efficient evaluation of building damage after a disaster. Considering this, high quality images of Malatya city before and after the earthquake were analyzed and data sets were created by masking using Mask RCNN deep learning method through ArcGIS Pro 3.4.0 software. According to the results of the research, it quickly detected damaged buildings with an accuracy rate of 70% according to satellite images after the earthquake. As a result, GIS and deep learning models were used to detect and map the initial damage after the earthquake.

In this study, the forest fire that occurred in Manavgat district of Antalya on 28 July 2021 and lasted for 15 days was analysed by remote sensing techniques using Landsat 8 satellite images. Satellite images of the study area dated July 2021 before the forest fire and August 2021 after the forest fire were obtained. Burnt areas were identified using data’s such as Normalized Burned Ratio (NBR) and Normalized Vegetation Index (NDVI) indices and Difference Normalized Burned Ratio (DNBR) and Difference Normalized Vegetation Index obtained by using the differences of these indices. The maximum similarity algorithm of pixel-based controlled classification was also applied to the data set. The area destroyed by burning after the forest fire was tried to be calculated with these two indexes. It was investigated whether the results of three different methods were compatible and consistent with the results of the General Directorate of Forestry. Although there are differences between the results, it was determined that the selected method and the materials used were suitable for such studies.

This is study is based with the support of RS-GIS technology on the land use of Samsun Center, as well as the coastal districts of Ilkadım,Atakum,Bafra Plain, through the processing and interpretation of satellite images in the summer months of 2000,2010,2020. Spatial and temporal variability properties of LU/LC were determined using MLC algorithm, controlled classification approach. The predictive values of the LU/LC change that will occur in 2030, calculated with the MLP‑ANN model based on Machine Learning algorithms and mapped with the QGIS 3.16 program. To determine the accuracy coefficient of the model, 2020 LU/LC simulation performed using the transition potential matrix of 2000 and 2010 LU/LC data. The results of simulation were compared the data of land use land cover with the 2020 to evaluate the accuracy of the simulation model. The model of MLP‑ANN provided an accuracy of 72% based on the kappa fit index. According to MLP‑ANN model 2030 results were an increase of 73.33 km² in built up areas, an increase of 56.89 km² in bare areas, and a decrease of 129.66 km² in green areas. It provided a reference basis for future Samsun urban to rural coastline LU planning and management and LU structure optimization.

Urban green spaces are areas established to meet the recreational needs of urban people. Although green spaces vary from country to country and region in terms of plan and design features, they were basically created to allow people to meet with nature. Parks are the basic components of urban landscapes that provide environmental and social functional value. Urban parks, in particular, provide spaces for outdoor physical activities. In order to take advantage of the opportunities of activities in the parks, users must have convenient access to these resources. One of the most important aspects for researching the use and potential benefits of urban green spaces is the assessment of their geographic accessibility. The widespread use of smart city systems and the gradual expansion of their usage areas increase the importance of spatial analysis. Spatial analyses are used in today’s urban management in the processes of determining social needs, identifying current problems, and putting forward solutions. When spatial analyses are used together with GIS, the field of application develops even more, and it supports local governments in responding to the changing demands of the society for a better life. In the study, the adequacy and accessibility of 160 city parks in Konyaaltı District of Antalya Province were examined. In terms of the adequacy of the parks, the area value of 10 m2 per person determined with the Construction Plan numbered 3194 was taken as basis. In terms of accessibility, distance values of 200, 400, 800, 1,200 meters were examined. Neighborhood boundaries and population information were obtained from the relevant units, and Arc-GIS software was used in the analysis. It was determined that the parks in Konyaaltı district were insufficient in terms of adequacy and accessibility. Finally, suggestions were made in terms of increasing the adequacy of the parks and ensuring accessibility.

The average precipitation on the irrigation field can be estimated from the Meteorology Observation Stations by using spatial interpolation methods such as Thiessen polygon and isohyetal curves. However, the fact that precipitation doesn't occur homogenous in spatial scales, spatial interpolation methodologies need a large number of meteorology stations for more accurate results. In recent years, remote sensing methods have diversified to estimate precipitation. In this study, performance of the satellite-based precipitation data was assessed to determine areal precipitation over an irrigation area. This study was conducted over left bank irrigation area located in the Çukurova Plain of Turkey. Relationship between CHIRPS satellite based on monthly precipitation data and 4 meteorology stations’ data were analyzed. Determination coefficients (R2) of the stations were found between 0.64 and 0.77, for point based comparison, R2 was calculated as 0.84 with Thiessen polygon method. It is concluded that the precipitation amount in the irrigated area can be estimated as accurately as classical methods such as Thiessen polygon with satellite-based precipitation data.