Main Article Content
Extraction of building areas and calculation of roof slopes is a data set used in many areas such as urbanism, pre-disaster and post-disaster situation, city information systems, urban transformation, infrastructure, etc. The data obtained with the help of unmanned aerial vehicles is used in many sectors and fields. Being fast and economical, sensitive and detailed data production makes unmanned aerial vehicles advantageous. In our study, 540 photographs were taken with a UAV in an area of 75,000 m², various image processing techniques used in remote sensing and various analysis methods used in geographic information systems, buildings were extracted and roof slopes were classified. In the study, first planning was made for the area, ground control points were established and flights were made. The data obtained after these processes were obtained by photogrammetric methods, point cloud, digital surface model, digital terrain model and normalized digital elevation model and orthophoto data. With the help of orthophoto data with (rgb) red, green and blue bands, vegetation areas were tried to be determined with the help of red green blue vegetation index. In the normalized digital elevation model (ndsm) data obtained from the digital terrain model and the digital elevation model difference, the 3m threshold value was accepted for building detection, and the calculation was made by accepting objects higher than this value as buildings. It was tried to exclude vegetation from the valuation by masking with the help of red green blue vegetation index. After this process, morphology filter was applied on raster data. The generated building data was converted into vectorial data and a point was drawn in the center of the buildings by using orthophoto data for accuracy analysis in the study. It has been determined that there are 729 real buildings in the study area. It has been determined that there are 686 building data produced as vector data. Spatial intersection analysis used in geographic information systems was made from the name of this process. With the help of this intersection analysis, accuracy and precision were determined in the study. In addition to removing the building areas, it is aimed to determine the roof slopes. For this reason, the slope of the roof areas was calculated using the nsdm data. The slope calculation process is classified into 10% sections, and each 10% slope group is included in a class. However, a homogeneous slope data could not be reached due to the day heat, small warehouses and various materials on the roofs. For this reason, using the obtained slope data and the generated vector building data, the most repeated value of the slopes from the roof areas of the building was calculated and accepted as the building roof slope. As a result, the building areas and the slopes of the tents of these buildings were determined with high accuracy.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Yakar, M., & Doğan, Y. (2017). Mersin Silifke Mezgit Kale Anıt Mezarı fotogrametrik rölöve alımı ve üç boyutlu modelleme çalışması. Geomatik, 2(1), 11-17. https://doi.org/10.29128/geomatik.296763
Villi, O., & Yakar, M. (2022). İnsansız Hava Araçlarının Kullanım Alanları ve Sensör Tipleri. Türkiye İnsansız Hava Araçları Dergisi, 4(2), 73-100. https://doi.org/10.51534/tiha.1189263
Karataş, L., Alptekin, A., & Yakar, M. (2022). Detection and documentation of stone material deterioration in historical masonry structures using UAV photogrammetry: A case study of Mersin Aba Mausoleum. Advanced UAV, 2(2), 51-64.
Karataş, L., Alptekin, A., Kanun, E., & Yakar, M. (2022). Tarihi kârgir yapılarda taş malzeme bozulmalarının İHA fotogrametrisi kullanarak tespiti ve belgelenmesi: Mersin Kanlıdivane ören yeri vaka çalışması. İçel Dergisi, 2(2), 41-49.
Alptekin, A., & Yakar, M. (2020). Determination of pond volume with using an unmanned aerial vehicle. Mersin Photogrammetry Journal, 2(2), 59-63.
Ünel, F. B., Kuşak, L., Çelik, M., Alptekin, A., & Yakar, M. (2020). Kıyı çizgisinin belirlenerek mülkiyet durumunun incelenmesi. Türkiye Arazi Yönetimi Dergisi, 2(1), 33-40.
Kanun, E., Alptekin, A., Karataş, L., & Yakar, M. (2022). The use of UAV photogrammetry in modeling ancient structures: A case study of “Kanytellis”. Advanced UAV, 2(2), 41-50.
Yakar, M., Ulvi, A., Yiğit, A. Y., & Alptekin, A. (2023). Discontinuity set extraction from 3D point clouds obtained by UAV Photogrammetry in a rockfall site. Survey Review, 55(392), 416-428. https://doi.org/10.1080/00396265.2022.2119747
Kanun, E., Alptekin, A., & Yakar, M. (2021). Cultural heritage modelling using UAV photogrammetric methods: a case study of Kanlıdivane archeological site. Advanced UAV, 1(1), 24-33.
Kokeza, Z., Vujasinović, M., Govedarica, M., Milojević, B., & Jakovljević, G. (2020). Automatic building footprint extraction from UAV images using neural networks. Geodetski vestnik. glasilo Zveze geodetov Slovenije, 64(4), 545-561. https://doi.org/10.15292/geodetski-vestnik.2020.04.545-561
Fan, X., Nie, G., Gao, N., Deng, Y., An, J., & Li, H. (2017, July). Building extraction from UAV remote sensing data based on photogrammetry method. In 2017 IEEE international geoscience and remote sensing symposium (IGARSS), 3317-3320. https://doi.org/10.1109/IGARSS.2017.8127707
Gürbüz, M. F., & Türker, M. (2017). Çok yüksek çözünürlüklü İHA görüntülerinden otomatik ağaç tespiti. Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, 17, 60-71.
Uzar, M., & Yastikli, N. (2013). Automatic building extraction using LiDAR and aerial photographs. Boletim de Ciências Geodésicas, 19, 153-171. https://doi.org/10.1590/S1982-21702013000200001
Guler, M. F. Turker, M. (2018). Automatic extraction and divisions of building roof planes from lidar data. Uzaktan Algılama – Coğrafi Bilgi Sistemleri Sempozyumları, 7017.
Agapiou, A. (2020). Vegetation extraction using visible-bands from openly licensed unmanned aerial vehicle imagery. Drones, 4(2), 27. https://doi.org/10.3390/drones4020027