No abstract

The main objective of the study was to examine accuracies of DEMs (Digital Elevation Models) with different topographical structures generated by using the Unmanned Aerial Vehicle (UAV) point clouds. Two different terrains with flat and sloping topographical structures were selected for the study, and DEMs of these terrains were generated using eight interpolation techniques (Kriging, Natural Neighbor, Radial Basis Function Triangulation with Linear interpolation, Nearest Neighbor, Invers Distance to a Power, Local Polynomial and Minimum Curvature). The accuracies of DEMs were tested by calculating the statistic methods with the help of the control points obtained by land surveying techniques. At the end of the study, it was observed that in DEMs prepared for both flat (study area 1) and sloping (study area 2) terrains, Kriging interpolation method yields the best results as study area 1 and 2, respectively. In addition, the results were examined using Shapiro-Wilk and ANOVA: Friedman tests. After observing with the Shapiro-Wilk test that the data has a normal distribution, it was statistically determined through the parametric ANOVA: Friedman test that there is no difference between the variables.

Local geoid determination studies are commonly carried out today to establish the relationship between the ellipsoidal height (h)$$ (h) $$ determined by satellite geodesy methods and the orthometric height (H)$$ (H) $$ found using geoid undulation (N)$$ (N) $$. The aim of this study was to determine a local geoid using the kriging, local polynomial (LP), and inverse distance to a power (IDP) interpolation methods along with the random forest (RF) regression method and to compare the performance. For the application, 193 Global Navigation Satellite System (GNSS)/leveling points homogeneously distributed over the study area were selected as reference data, and 70 GNSS/leveling points were selected as test data. The results were compared in terms of accuracy using well‐known performance metrics, namely, the root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) values. According to the results, the highest R2 and the lowest RMSE and MAE values were obtained with the RF regression method. These findings demonstrated the superiority of the RF regression over the classic interpolation methods applied in the local geoid determination for the study area.

This study aimed to determine the most suitable local geoid model based on 641 GNSS/leveling points within the borders of Kars Province in eastern Turkey using the generalized regression neural network (GRNN), weighted average (WA), multiquadric (MQ), inverse multiquadric (IMQ) function, and local polynomial (LP) method. Among these methods used in local geoid determination, the studies conducted with the GRNN method are very limited in the literature. To test the performance of the model, 169 GNSS/leveling points were selected as test data. When selecting reference points and test points, care was taken to distribute these points homogeneously within the study area. The criteria of root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R 2 ) were used to assess the accuracy and error rates of the results achieved using the different methods. According the results of analysis, GRNN method yielded better results than other interpolation methods. These results have showed that GRNN method can be taken into account in modeling various geodesy problems.

Son yıllarda insansız hava araçlarının (İHA), hem yüksek konumsal çözünürlükte hem de çok bantlı görüntü sağlaması sayesinde bu araçların kullanımı giderek yaygınlaşmaktadır. Klasik fotogrametrik haritalama yöntemlerine göre İHA’ ların haritalama maliyetinin düşük olması, istenilen zaman ve sıklıkta görüntü elde edilebilmesi, kullanıcılar için bu araçların daha çok tercih edilmesini sağlamaktadır. Bunun yanında günümüzde İHA' lara gerçek zamanlı kinematik (GZK) konumlandırma sistemleri de takılabilmekte ve bu sayede hassas konum bilgisine sahip görüntüler elde edilebilmektedir. Bu da İHA ile üretilen fotogrametrik ürünlerin doğruluğunun, yersel ölçme yöntemleri ile elde edilen sonuçlara daha yakın olmasını mümkün kılmaktadır. Yapılan çalışmada Erzincan Binali Yıldırım Üniversitesi Yalnızbağ Yerleşkesi için SenseFly eBeeX İHA ile üretilen ortofotonun konum doğruluğunun araştırılması amaçlanmıştır. Üretilen ortofotonun konum doğruluğu için çalışma alanına rasgele ve homojen olarak dağılmış 100 nokta, görüntü üzerinde işaretlenmiştir. Bu noktaların hem hâlihazır harita üzerinden hem de ortofoto üzerinden Y ve X koordinatları alınmış ve koordinat farklarından ortofotonun konum doğruluğu hesaplanmıştır. Sonuç olarak üretilen ortofotonun konum doğruluğu Y ve X yönlerinde my = ± 1.0 cm, mx = ± 0.8 cm, yatayda ise mk = ±1.3 cm olarak hesaplanmıştır. Bu sonuç bize üretilen ortofotonun birçok mühendislik projesinde altlık olarak kullanılabileceğini göstermiştir.