Sürdürülebilir Kalkınma Amaçları

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• MakaleAçık Erişim2026/r/gumushane/15958
  Assessment of topographically influenced gravity-height gradients and plumb line curvature at Sümela Monastery (Türkiye)

  Tütüncü, Mehmet; Yilmaz, Nazan

  Accurate estimation of gravity-height gradient and plumb line curvature is fundamental for gravity field modeling, geoid determination, and high-precision geodetic applications, especially in mountainous regions. This study presents an integrated computational framework combining terrestrial gravity observations, precise leveling, GNSS measurements, and Global Geopotential Models (GGMs) to estimate and compare gravity-height gradient and plumb line curvature in the rugged terrain surrounding S & uuml;mela Monastery. Simultaneous gravity and leveling surveys were conducted during two observation periods to compute actual and normal gradient, level surface curvature, and plumb line curvature. Additionally, twelve GGMs obtained from the ICGEM service were used to derive the same parameters through spherical harmonic expansions. A comprehensive statistical evaluation, including minimum, maximum, mean, standard deviation, and RMSE, was performed to assess the consistency between terrestrial and model-based results. The results show that normal gradient and level surface curvature from GGMs generally agree well with terrestrial data, whereas larger discrepancies occur in actual gradient and plumb line curvature in areas with steep topographic gradients. CHAMP-based models demonstrate comparatively better consistency in representing plumb line curvature. The proposed framework confirms the reliability of global models at regional scales while emphasizing the importance of terrestrial data in complex topographic environments.

  İncele
• MakaleAçık Erişim2025/r/gumushane/14299
  DAPNet++: density adaptive PointNet + + for airborne laser scanning data

  Akbulut, Zeynep; Karsli, Fevzi

  Addressing the challenges arising from the irregularity and varying density of Airborne Laser Scanning (ALS) point clouds, which particularly affect the performance and generalization ability of 3D Deep Neural Networks (DNNs), is essential for their effective application in direct semantic segmentation tasks. In this study, we investigated the underexplored aspect of adapting PointNet + + for semantic segmentation of ALS point clouds. We introduced Density-Adaptive PointNet++ (DAPNet++) to enhance robustness against variable point densities through modifications made to the original PointNet + + architecture. Our methodology includes controlled block partitioning based on point density replacing the original batching strategy. Another key advancement in the study is the automation of the initial value of the neighborhood search radius by taking into account the characteristics of the dataset. This approach optimizes receptive field determination, crucial for effective semantic segmentation. The effectiveness of DAPNet + + is validated through extensive experiments on various datasets, including ISPRS Vaihingen, DALES, subsampled DALES, and OpenGF. Notable improvements include up to 11% increase in weighted mean Intersection-over-Union (mIoU) on the highly variable OpenGF test dataset and 3% increase in mIoU on the subsampled DALES dataset. Furthermore, the generalization capability of the DAPNet + + was tested, revealing an approximately 5% improvement in evaluation metrics compared to PointNet++. In summary, DAPNet + + minimizes trial-and-error in the selection of parameters for block partitioning and radius, and it enhances robustness against variable point density. With the proposed approach, it has been observed that there is a significant improvement in accuracy for underrepresented classes, mitigating class imbalance in ALS point clouds.

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• MakaleAçık Erişim2025/r/gumushane/14116
  Post-seismic structural assessment: advanced crack detection through complex feature extraction using pre-trained deep learning and machine learning integration

  Reis, Hatice Catal; Turk, Veysel; Ustuner, Mustafa; Kaya Yildiz, Cagla Melisa; Tatli, Ramazan

  Earthquakes can often cause significant damage to buildings. After an earthquake, experts/managers need to make quick and accurate damage assessments. Traditionally, manual analysis processes have been widely used in damage assessment studies. The fact that these methods are time-consuming and based on human observation leads to certain limitations in damage assessment studies. In recent years, artificial intelligence techniques such as deep learning and machine learning have frequently been preferred in damage detection studies, and significant success has been achieved. This study aimed to automatically detect cracks/damages in the buildings in Diyarbakir city after the February 6, 2023 Kahramanmaras, Turkey earthquake. Our experimental dataset was collected by the researchers and named Kahramanmaras-Diyarbakir Earthquake Building Crack Dataset (KDBECD-2023). The data set consists of four categories in terms of damage level: undamaged, slightly damaged, moderately damaged, and heavily damaged buildings. DenseNet201 deep learning architecture and popular machine learning algorithms, Support Vector Machine, Random Forest, Decision Tree, and K-Nearest Neighbors (KNN) were used to classify cracks at different damage levels. In the experimental phase, feature extraction was performed with the DenseNet201 architecture. In addition, dimensional reduction was applied with the Principal Component Analysis method to reduce the computational complexity of the proposed hybrid study. According to the experimental results, the DenseNet201-KNN hybrid model gave the most successful result with an accuracy value of 94.62%. The results of this study can make important contributions to decision makers and experts in detecting cracks and damages in buildings after an earthquake.

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• MakaleYalnız künye2021/r/gumushane/13337
  Investigating the historical development of accuracy and precision of Galileo by means of relative GNSS analysis technique

  Yalvac, Sefa

  Galileo navigation system has significantly expanded satellite constellation over the last few years and now serves with 22 satellites in three orbital planes. The development of the Galileo is not only limited to the number of satellites in the space segment, but also the quality of the IGS MGEX products produced by different Analysis Centers has increased gradually and brought up the topic of investigation of the accuracy and precision of the Galileo-only positioning. However, almost all of the studies on Galileo-only positioning in the literature have been performed with Precise Point Positioning method and comprehensive studies with the relative positioning technique are quite a few. In this study, the development of the accuracy and precision of the Galileo from 2016 to the end of 2019 (4 years) has been investigated for the first time by means of relative positioning technique. For this purpose, data of eight GNSS stations selected from the IGS MGEX network were obtained and analyzed with GAMIT/GLOBK software as GPS-only and Galileo-only, separately. Using the daily solutions obtained from both navigation systems, time series were produced and the differences between them were compared in order to reveal the Galileo accuracy. According to the results, the accuracy of the Galileo was variable during the experiment and it could be examined in three periods. The averages of 3D positioning error in the first period (2016-2017) is 14.92 mm and improved by 56%, and decreased to 6.55 mm in the second period (2017-2018), and then improvement at 23% in the third period (2018 to the end of 2019) and decrescent to level of 5.13 mm on average for all stations. Similarly, standard deviations obtained from Galileo increased during the experiment and during the 3rd period it was closed to a few mm with GPS. It was concluded that the accuracy and precision of the Galileo showed parallel development and the development was directly related to the number of satellites in the Galileo constellation, and as a result, nowadays, Galileo-derived coordinates are close to the coordinates obtained from the GPS by a few mm.

  İncele
• MakaleYalnız künye2018/r/gumushane/15129
  Multipath interference cancelation in GPS time series under changing physical conditions by means of adaptive filtering

  Üstün, Aydın; Yalvaç, Sefa

  Multipath, a highly autocorrelated signal is observable phenomena during time periods longer than the sidereal period of Global Positioning System (GPS) satellites in their constellations. Multipath pattern and sidereal repeat time interaction are examined in terms of time series correlations of topocentric coordinates using GPS code and phase observations collected at high and low frequencies. The horizontal and vertical components at 5 and 30 s sampling rates are analyzed to detect and remove multipath classified by their MP1 levels and baseline lengths. For the selected 11 stations that have 24-hour data from CORS stations in the USA and Turkey, the repeat times of the GPS constellation were identified as a function of maximum cross-correlation of adjacent time series. The multipath interference signal from two-day time series data is removed by an adaptive filter to improve time series of coordinate estimates. After the filtering process, an effective epoch based position tracking has been accomplished, especially in the height component. The position errors disappeared to maximum possible extent in the pseudo-range measurements. Up to 70% improvement for the phase derived coordinates in reflective environment has been achieved over the baselines ranging from 1 km to 175 km. For the phase observables, the filter success directly related to the multipath level which is specific to the station.

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