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- MakaleAçık Erişim2023Investigation of geometric object and indoor mapping capacity of Apple iPhone 12 Pro LiDAR
Günen, Mehmet Akif; Erkan, İlker; Aliyazıcıoğlu, Şener; Kumaş, Cavit
LiDAR (light detection and ranging) sensors use laser beams to calculate distances in the surroundings. These sensors can be applied to a wide range of tasks, and they are frequently helpful in tasks like building 3D maps, navigating airplanes, robots, conducting mining operations, and automated driving. High-resolution distance measurements are taken by LiDAR sensors, but they also gather environmental data. This information aids in locating, identifying, and quantifying things and their surroundings. The iPhone 12 Pro, which Apple released in 2020, was evaluated for accuracy with various geometric shapes and its capacity to recognize indoor environments. Free of charge 3D Scanner and the Clirio Scan application were employed in this situation. However, it was found that the root mean square error and mean error in indoor mapping were ±1.41 cm and-0.56 cm in 3D Scanner and ±3.94 cm and-0.60 cm in the Clirio Scan application, respectively, despite the findings obtained showing low accuracy in scanning small geometric objects due to the scanning difficulty. Clirio does not reject the null hypothesis in the t-test that was conducted. The accuracy of the LiDAR sensor in indoor mapping has been shown to be more promising than that of small items. In order to evaluate the reliability and reusability of the indoor mapping application according to reference measurements, intraclass correlation test was performed and the results were determined to be reliable.
- MakaleYalnız künye2019Mapping Istanbul quarry mining region using Landsat 8 OLI&TIRS data
Balcik, Filiz Bektas; Topaloglu, Raziye Hale; Goksel, Cigdem
Open mining operations have brought number of environmental challenges, which may result in soil erosion, dust, noise, water pollution, and severe impacts on biodiversity. Determination of the mining activities and its surrounding environment is a major issue in sustainable resource management. Many remote sensing indices have been developed and widely applied as an effective tool to determine different land surface types. The main objective of this study was to determine quarry mines and its surroundings land use and land cover (LULC) categories in the selected study area of Istanbul using different remote sensing indices including Tasseled Cap Transformation (Brightness, Greenness and Wetness) and Normalized Difference Bareness Index (NDBals). In this study, an open mining area in the northern Black Sea coastal part of Istanbul was selected as study area. Freely available Landsat 8 OLI & TIRs image was used to determine quarries and land cover/ land use categories in the concerned area. By using original bands of Landsat 8 OLI, TCT components and NDBal, three different data sets were created. Each image was then separately subjected to support vector machine classification and processed to identify and quantify LULC categories. The accuracy of classification results were evaluated in a comparitive manner using error matrix. Data set C had the highest overall accuracy and Kappa statistics that were calculated as 83.00 and 0.80, respectively.