Sürdürülebilir Kalkınma Amaçları

• 1

Yazarlar

• Comert, Resul(2)
• Abdulmuttalib, H.M.(1)
• Avdan, Ugur(1)
• Di, K.(1)
• Faruque, F.S.(1)
• Gorum, Tolga(1)
• Honkavaara, E.(1)
• Jiang, J.(1)
• Kaplan, Onur(1)
• Komp, K.(1)
• Li, R.(1)
• Liang, X.(1)
• Nefeslioglu, Hakan A.(1)
• Osmanoglu, B.(1)
• Peled, A.(1)
• Scaioni, M.(1)
• Shaker, A.(1)
• Soergel, U.(1)
• Stilla, U.(1)
• Tanzi, J.J.(1)
• Wu, L.(1)
• Yoshimura, M.(1)
• Zhang, H.(1)
• Zhang, J.(1)

• MakaleYalnız künye2019Scopus SJRQ1/r/gumushane/12659
  Mapping of shallow landslides with object-based image analysis from unmanned aerial vehicle data

  Comert, Resul; Avdan, Ugur; Gorum, Tolga; Nefeslioglu, Hakan A.

  The Black Sea Region of Turkey is one of the most landslide prone areas due to its high slope topography, heavy rainfall, and highly weathered hillslope material conditions. Preparation of landslide inventory maps is the first step in producing landslide susceptibility maps. Ground-based methods for mapping landslide occurrences are time-consuming and expensive. Additionally, landslide mapping based on satellite imageries and aerial photographs has some limitations, including climatic conditions, cost, and limited repetitive measurement capacity. Visual interpretation-based landslide mapping, which is based on satellite imageries and aerial photographs, is a time-consuming procedure that requires an experience-based expert opinion. Therefore, the data acquisition based on unmanned aerial vehicle (UAV) and landslide event inventory maps using an object-based classification approach can be superior to other methods in terms of speed and cost. In this study, we developed a semiautomatic model using object-based image analyses for rapid mapping of shallow landslides from the data obtained from UAVs after major landslide events in the Black Sea Region of Turkey. For this purpose, two test sites—Kurucasile (Bartin) and Cayeli (Rize)—were selected. Landslide mapping models were developed in the investigation sites, and the performance of the models was evaluated. The landslides' data obtained with the developed models were compared to the landslides' data produced by the experts. The comparison process revealed that landslides mapped by using UAV data have an accuracy rate higher than 86% according to the number of landslides and 83% according to the landslide area.

  AtıfScopus76·WoS55·OpenAlex70

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• BildiriAçık Erişim2018/r/gumushane/13086
  OBJECT BASED BUILDING EXTRACTION and BUILDING PERIOD ESTIMATION from UNMANNED AERIAL VEHICLE DATA

  Comert, Resul; Kaplan, Onur; Honkavaara, E.; Komp, K.; Stilla, U.; Jiang, J.; Zhang, H.; Peled, A.; Soergel, U.; Scaioni, M.

  The aim of this study is to examine whether it is possible to estimate the building periods with respect to the building heights in the urban scale seismic performance assessment studies by using the building height retrieved from the unmanned aerial vehicle (UAV) data. For this purpose, a small area, which includes eight residential reinforced concrete buildings, was selected in Eskisehir (Turkey) city center. In this paper, the possibilities of obtaining the building heights that are used in the estimation of building periods from UAV based data, have been investigated. The investigations were carried out in 3 stages; (i) Building boundary extraction with Object Based Image Analysis (OBIA), (ii) height calculation for buildings of interest from nDSM and accuracy assessment with the terrestrial survey. (iii) Estimation of building period using height information. The average difference between the periods estimated according to the heights obtained from field measurements and from the UAV data is 2.86 % and the maximum difference is 13.2 %. Results of this study have shown that the building heights retrieved from the UAV data can be used in the building period estimation in the urban scale vulnerability assessments.

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  AtıfScopus9·OpenAlex10

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