Cultural heritage UAV 3D Model Photogrammetry Circular mission planHistorical artifacts are in danger all over the world as they are being damaged continuously. In order to transfer them to future generations and restore them, we need to create digital products of them. The developments in remote sensing allow us to model objects in computer environment. In this study, a cultural heritage located in Mersin was modelled in 3D using an unmanned aerial vehicle (UAV). Circular mission plan in the mobile phone application was preferred. Overlapping pictures were aligned. Digital surface model, orthophoto and 3D model of Ucayak Ruins have been created. Aerial photogrammetry allows us to create digital products in a short time. The obtained model will be used in tourism advertisements.

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Compression and swelling index parameters, obtained from consolidation test, are used to calculate settlement for normally and over-consolidated soils respectively. When the conditions are not suitable to perform that test, various alternative methods are investigated to get those parameters without carrying out the consolidation test. In this study, a data set including 18 marine and 40 terrestrial undisturbed Quaternary sediments was taken from southern parts of Mersin City, Turkey. Parameters obtained from consolidation test and index tests were correlated by applying simple and multiple regression analyses. The initial void ratio is the main determiner for estimating both the compression and swelling index parameters. Although attempts have been made to correlate parameters with wide distribution of samples, there is no study done with narrow range. The database was divided to subgroups according to the Plasticity chart to obtain more reliable equations. To test the significance of regression analyses, T and F-tests were done. With this study, statistically significant new equations with very high correlation coefficients are proposed.

In this paper, an inventory of the landslide that occurred in Karahacılı at the end of 2019 was created and the pre-landslide conditions of the region were evaluated with traditional statistical and spatial data mining methods. The current orthophoto of the region was created by unmanned aerial vehicle (UAV). In this way, the landslide areas in the region were easily determined. According to this, it was determined that the areas affected by the landslides had an average slide of 26.56 m horizontally. The relationships among the topographic, hydrographic, and vegetative factors of the region were revealed using the Apriori algorithm. It was determined that the areas with low vegetation in the study area with 55% confidence were of a Strong Slope feature from the Apriori algorithm. In addition, the cluster distributions formed by these factors were determined by K-means. Among the five clusters created with K-means, it was determined that the study area was 38% in the southeast, had a Strong Slope, Low Vegetation, Non-Stream Line, and a slope less than 140 m. K-means results of the study were made with performance metrics. Average accuracy, recall, specificity, precision, and F-1 score were found as 0.77, 0.69, 0.84, and 0.73 respectively.

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