Arıkan İspir, Duygu
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Name Variants
Arikan, D. Arıkan İspir , D. Arıkan, D. Arıkan İspir , Duygu Arikan, Duygu
Job Title
Email Address
darikan@ktun.edu.tr
Main Affiliation
02.08. Department of Geomatic Engineering
Status
Current Staff
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID
Research Topics
Domains
Physical Sciences
Fields
EngineeringEnvironmental Science
Subfields
Media TechnologyHealth, Toxicology and MutagenesisGlobal and Planetary ChangeEnvironmental Engineering
Specific Research Areas
Remote-Sensing Image Classification
Air Quality and Health Impacts
Atmospheric aerosols and clouds
Urban Heat Island Mitigation
Remote Sensing and LiDAR Applications
Fire effects on ecosystems
Sustainable Development Goals
1NO POVERTY
0
Research Products
2ZERO HUNGER
0
Research Products
3GOOD HEALTH AND WELL-BEING
2
Research Products
4QUALITY EDUCATION
0
Research Products
5GENDER EQUALITY
0
Research Products
6CLEAN WATER AND SANITATION
1
Research Products
7AFFORDABLE AND CLEAN ENERGY
1
Research Products
8DECENT WORK AND ECONOMIC GROWTH
0
Research Products
9INDUSTRY, INNOVATION AND INFRASTRUCTURE
0
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10REDUCED INEQUALITIES
0
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11SUSTAINABLE CITIES AND COMMUNITIES
5
Research Products
12RESPONSIBLE CONSUMPTION AND PRODUCTION
1
Research Products
13CLIMATE ACTION
2
Research Products
14LIFE BELOW WATER
2
Research Products
15LIFE ON LAND
0
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16PEACE, JUSTICE AND STRONG INSTITUTIONS
0
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17PARTNERSHIPS FOR THE GOALS
0
Research Products
Documents
4
Citations
10
h-index
1
Documents
5
Citations
9
Publication Collaboration
| Affiliation Name | Count |
|---|---|
| Konya Technical University | 13 |
1 / 1
Data obtained from OpenAlex
Scholarly Output
13
Articles
12
Views / Downloads
30/48
Supervised MSc Theses
0
Supervised PhD Theses
0
WoS Citation Count
9
Scopus Citation Count
10
Patents
0
Projects
0
WoS Citations per Publication
0.69
Scopus Citations per Publication
0.77
Open Access Source
9
Supervised Theses
0
| Journal | Count |
|---|---|
| Remote Sensing Applications-Society and Environment | 2 |
| Turkish Journal of Remote Sensing | 2 |
| DYSONA – Applied Science | 1 |
| Earth Science Informatics | 1 |
| Harita Dergisi | 1 |
Current Page: 1 / 3
Scopus Quartile Distribution
Competency Cloud
13 results
Scholarly Output Search Results
Now showing 1 - 10 of 13
Article The Effect of Various Interpolation Methods Applied at Aerial Lidar Data on Dtm Accuracy(Konya Technical University, 2021-06-01) Arıkan, Duygu; Yıldız, Ferruh; Makineci, Hasan Bilgehan; İspir, Duygu ArıkanMany application areas have approved Digital Terrain Models (DTM) as a subject. It is primarily used in many sectors, such as civil engineering studies, geographical information systems. When producing DTMs, they should be fast-produced, as they should always be up-to-date, sufficient accuracy for users and economical to manufacture. One of the essential factors affecting the accuracy of DTM is the selected interpolation method. In this study, weighted average interpolation, polynomial interpolation, multi quadratic interpolation, linear interpolation in the network of triangles, small curvature surface interpolation, and the nearest neighbor interpolation methods were explained theoretically and then the points of the area were tested in the model with the Surfer program. The comparison of standard deviation results of these six different interpolation methods, which are frequently used in DTM studies in the literature, is the most crucial purpose of the study. A sigma test was used to eliminate unsuitable measurements on the results. Then, two different areas were determined, with a total of 1250 points in a fixed area on the investigated area. The number of bases and samples for these areas are arranged differently. Then, using each interpolation method, the land model was reexamined, and the result between them was evaluated. As a result of the researches, it is discovered that the weighted-average method gives better results than others.Conference Object Citation - WoS: 1Spatial and Temporal Analysis of Pollutant Gases in Western Black Sea of Turkiye(Copernicus Gesellschaft Mbh, 2023-08-15) Arikan, D.; Yildiz, F.Environmental pollution, particularly air pollution, is one of the foremost problems we face today. Air pollution has become a global issue that affects not only regional areas but also the entire planet. The increase in the amount and concentration of pollutants or harmful substances in the atmosphere, such as various gases, particulate matter, and water vapor, causes air pollution. The rise in these substances can be due to human activities or natural environmental factors. It is crucial to examine air quality to reduce the harm inflicted on living and non-living entities. In this study, the spatial and temporal analysis of air pollutants (CO, NO2, UV_AER) in the Western Black Sea region was conducted using the Sentinel-5 TROPOMI satellite sent to monitor climate change and air quality. The Google Earth Engine platform was used to obtain the data. Monthly pollution maps were created for the year 2022, and the primary sources of pollutants were analysed. As a result, it was observed that pollutants changed on a monthly and seasonal basis, and areas with high pollutant concentrations in the region were identified. Mining, industrial activities, transportation networks, and domestic activities were determined to be the primary sources of air pollution in the study area.Article Göktürk-1 Uydu Görüntülerinden U-net Modeli Kullanılarak Binaların Segmentasyonu(2023-06-23) Arıkan Duygu; Yıldız FerruhNüfus artışı, kentsel bölgelerde plansız yapılaşmanın ortaya çıkmasına yol açmaktadır. Bu durum dünya genelinde bir sorun haline gelmiştir. Bu alanların belirlenmesi ve tespit edilmesi, kentsel yönetim ve yeniden yapılanma planlaması için büyük öneme sahiptir. Ancak bu işlemler, arazide maliyetli ve zaman alıcı olabilmektedir. Uzaktan algılama görüntüleri kullanarak kentsel ve kırsal bölgelerde plansız yapılan binaları otomatik olarak tespit etmek ve karakterize etmek oldukça zordur. Son zamanlarda, derin öğrenme yöntemleri sayesinde karmaşık binaların tespiti mümkün hale gelmiştir. Bu çalışmada, Ankara'nın Etimesgut ilçesinden bir bölgenin bina çıkarımı işlemi, U-Net derin öğrenme mimarisi kullanılarak gerçekleştirilmiştir. İşlem için Inria Aerial Image Labeling adlı hazır bir veri seti kullanılmıştır. Eğitim işlemi için farklı sayıda görüntü (500, 1000, 2500, 5000) seçilmiştir. En iyi öğrenme sonucu, 0.5 m uzamsal çözünürlüğe sahip Göktürk-1 uydu görüntüleriyle test edilmiştir. Sonuçlara göre, U-Net modelinin bina segmentasyonunda Jaccard katsayısı 0.862, Dice benzerlik oranı 0.813 olarak bulunmuştur. Hazır veri seti kullanılarak U-Net modelinin derin öğrenme yöntemleri için kullanılabilir olduğu kanıtlanmıştır. Bu çalışma, kentsel alanlardaki binaların tespiti ve haritalanmasında derin öğrenme yöntemlerinin etkinliğini ve potansiyelini göstermiştir.Article Citation - WoS: 8Citation - Scopus: 7Investigation of Antalya Forest Fire's Impact on Air Quality by Satellite Images Using Google Earth Engine(Elsevier, 2023-01-01) Arıkan İspir, Duygu; Yıldız, Ferruh; Arikan, DuyguSeveral fires occur in Turkiye every year for various reasons. Some of these fires are extinguished within a short period of time. But some of them get bigger due to various factors. Prolonged fires harm the biological ecosystem and nature to a great extent. Gases released into the atmosphere due to the fires negatively affect the life of living things. In this study, the number of fires in the Antalya-Manavgat region in 2021 was determined. For this aim, BAI (Burned Area Index), NDVI (Normalized Difference Vegetation Index), SAVI (Soil Adjusted Vegetation Index) plant indexes were used to determine the amount of fire by us -ing high resolution satellite images. In addition, the carbon monoxide (CO) and ultraviolet aerosol index (UVAI) values during the fire were investigated using satellite images. The precipi-tation and temperature values of the region between the years 2015-2021 were discussed. The emitted gases were correlated with the national air quality station data and when the destroyed area after the fire was determined by satellite, it was determined that the CO gas released in the fire produced meaningful results with the national air quality station.Article Citation - Scopus: 1Using Deep Learning Algorithms For Built-up Area Extraction From High-resolution Göktürk-1 Satellite Imagery(Springer Heidelberg, 2024-12-11) Arikan Ispir, Duygu; Yildiz, FerruhBuilding extraction is a method used in high-resolution remote sensing image processing for urban planning and demographic analysis. This method plays a significant role in identifying urban structures and assessing damage in disaster situations. In this study, building extraction was performed using the U-net architecture with G & ouml;kt & uuml;rk-1 satellite imagery. This dataset was chosen because it has not been previously used in the literature for building extraction using deep learning methods. The impact of parameters such as the number of epochs, batch size, and learning rate on the results was evaluated. Additionally, the outcomes of the Adam (Adaptive Moment Estimation), SGD (Stochastic Gradient Descent), RMSprop (Root Mean Square Propagation), and Nadam (Nesterov-accelerated Adaptive Moment Estimation) optimizers were assessed. A training model was created using the Inria dataset, and the results were evaluated on G & ouml;kt & uuml;rk-1 satellite imagery. The findings indicated that increasing the number of epochs led to higher accuracy. The deep neural network model was applied to an urban area selected in Ankara using G & ouml;kt & uuml;rk-1 satellite imagery. Models for building segmentation and classification were both trained and tested. The results revealed that the U-net model achieved an overall accuracy of over 83.85% for building segmentation with the Adam optimizer. The intersection over union (IoU) ranged between 96 and 99%. The highest Dice similarity value (88.81%) was obtained with the RMSprop optimizer at 100 epochs.Article Citation - Scopus: 1Segmentation Of Buildings Using U-net Model From Göktürk-1 Satellite Images;(Osman Orhan, 2023-06-23) Arikan, D.; Yildiz, F.The increase in population has led to unplanned urbanization in urban areas, becoming a global issue. The identification and detection of these areas are of great importance for urban management and redevelopment planning. However, these processes can be costly and time-consuming when conducted on-site. Automatic detection and characterization of unplanned buildings in urban and rural areas using remote sensing imagery is a challenging task. Recently, with the advancements in deep learning methods, the detection of complex buildings has become possible. In this study, the building extraction process of a region from the Etimesgut district of Ankara was performed using the U-Net deep learning architecture. The Inria Aerial Image Labeling dataset, a publicly available dataset, was used for the process. Different numbers of images (500, 1000, 2500, 5000) were selected for the training process. The best learning outcome was tested with Göktürk-1 satellite imagery with a spatial resolution of 0.5 m. According to the results, the U-Net model achieved a Jaccard coefficient of 0.862 and a Dice similarity coefficient of 0.813 for building segmentation.The effectiveness and potential of deep learning methods were demonstrated using the U-Net model with the available dataset. This study showcased the efficiency and potential of deep learning methods in the detection and mapping of buildings in urban areas. © Author(s) 2023.Article Spatiotemporal Monitoring of the Surface Water Level Dynamics in Lake Tuz, Türkiye, Using Remote Sensing and GIS(2025-07-01) Arıkan İspir, DuyguArticle Investigation of Urban Heat Island and Carbon Monoxide Change Using Google Earth Engine in Konya(2023-07-03) Arıkan, Duygu; Yıldız, Ferruh; İspir, Duygu ArıkanThe increasing population has been causing changes in and around urban areas. As a result of this situation, it is observed that the amount of heat and air pollution in cities is higher than in rural areas. Urban heat islands (UHI) are a factor that affects people's quality of life. Therefore, monitoring temperature changes and taking regional measures is necessary. In this study, urban heat island (UHI) and carbon monoxide (CO) levels were determined using Landsat 8 satellite for the years 2019-2021 and Sentinel-5P satellite, respectively. The central districts of Konya were selected as the study area. To determine the urban heat island (UHI), surface temperature (ST) maps were created using the thermal band of a total of 12 Landsat 8 satellite images for each season. Additionally, 36 CO maps were generated using the Sentinel-5P satellite for the same region, covering twelve months. Upon evaluation of the generated maps, a significant correlation between temperatures and CO was observed. It was determined that areas with higher surface temperature also exhibited higher levels of carbon monoxide.Article Determination of Slow Surface Movements Around the 1915 Çanakkale Bridge During the 2022-2024 Period with Sentinel-1 Time Series(MDPI, 2026-03-11) Ispir, Duygu Arikan; Makineci, Hasan Bilgehan; Arikan Ispir, DuyguHighlights What are the main findings? Significant LOS displacement is mainly around the Lapseki approach, while the main bridge spans are structurally stable. Annual displacement trends correlate with seasonal environmental factors, showing distinct yearly variations. What are the implications of the main findings? Annual deformation remains within predicted engineering limits, confirming that the bridge's structural health is performing as expected under operational loads. Satellite interferometry is validated as a robust tool for monitoring megastructures, providing high-precision stability assessments without the need for costly in situ measurements.Highlights What are the main findings? Significant LOS displacement is mainly around the Lapseki approach, while the main bridge spans are structurally stable. Annual displacement trends correlate with seasonal environmental factors, showing distinct yearly variations. What are the implications of the main findings? Annual deformation remains within predicted engineering limits, confirming that the bridge's structural health is performing as expected under operational loads. Satellite interferometry is validated as a robust tool for monitoring megastructures, providing high-precision stability assessments without the need for costly in situ measurements.Abstract This study applied SBAS-InSAR to a dense Sentinel-1 Single Look Complex (SLC) archive (146 scenes) to monitor the 1915Çanakkale Bridge between 2022 and 2024 (data up to 7 January 2025 were available and considered in the time-series reconstruction). The analysis produced LOS mean velocity maps and pointwise displacement time series, revealing localized displacement concentrated near the Lapseki approach. Extreme LOS values reached approximately -101 mm (min) and +77 mm (max) across the domain, while maximum cumulative LOS displacement near the Asian anchorage approached -90 mm. These satellite observations suggest that ground-related processes may contribute to the detected observed movement; however, LOS-only measurements and limited in situ validations preclude a definitive separation between structural and geotechnical drivers. We therefore recommend targeted GNSS/levelling campaigns, ascending (ASC)-descending (DSC) InSAR fusion, and formal uncertainty reporting to better constrain the deformation sources and magnitude. The study concluded that the SBAS-InSAR method is effective for long-term, contactless monitoring of bridges and similar mega structures. It was also determined that this method can be used to identify critical areas requiring ongoing monitoring.Article Citation - Scopus: 1Seyfe Lake Seasonal Drought Analysis for the Winter and Summer Periods Between 2017 and 2022(Elsevier, 2024-04-01) Makineci, Hasan Bilgehan; Arikan, DuyguThis study focused on the effects of the increasingly prevalent drought phenomena in Central Anatolia every year on Lake Seyfe in Kirsehir. The drought has been investigated through change detection using Multispectral (MSI) PlanetScope remote sensing data between 2017 and 2022. Changes in wetlands, moist regions, swamps, agricultural areas, and drought over the summer and winter were observed using the normalized difference water index (NDWI), normalized difference vegetation index (NDVI), and soil adjusted vegetation index (SAVI) indices applied to mosaic data. The findings of the temporal variation over the summer and winter seasons are displayed as pixel -based spatial variations. The analysis used surface temperature, monthly temperature change tables, and precipitation maps for the years operated in the study. According to the research, the wetlands, which comprised 5% of the total surface area in the summer of 2020, were prevented from drying out entirely by the usual wet winters of 2021 and 2022.