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Tarihi yarımada'da turizm amaçlı, mekansal tabanlı sanal gerçeklik olanaklarının araştırılması

Research on spatial-based virtual reality opportunities for tourism purposes in the historical peninsula

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  1. Tez No: 837034
  2. Yazar: SANÇAR BUHUR
  3. Danışmanlar: PROF. DR. NEBİYE MUSAOĞLU
  4. Tez Türü: Doktora
  5. Konular: Jeodezi ve Fotogrametri, Geodesy and Photogrammetry
  6. Anahtar Kelimeler: Belirtilmemiş.
  7. Yıl: 2023
  8. Dil: Türkçe
  9. Üniversite: İstanbul Teknik Üniversitesi
  10. Enstitü: Lisansüstü Eğitim Enstitüsü
  11. Ana Bilim Dalı: Geomatik Mühendisliği Ana Bilim Dalı
  12. Bilim Dalı: Geomatik Mühendisliği Bilim Dalı
  13. Sayfa Sayısı: 119

Özet

Üç boyutlu (3B) kent modellerinin yaygınlaşması, kentlere ilişkin mekansal bilgilerin 3B olarak sunulmasını ve dolayısıyla kullanıcıların mekansal algılarının artırılmasını mümkün hale getirmiştir. Coğrafi verilerin 3B olarak dijital ortamlarda sunulmaya başlamasıyla birlikte büyük hacimli veri setlerinin saklanması, paylaşılması, sunulması ve farklı uygulamalar için farklı ayrıntı düzeylerine ihtiyaç duyulması gibi sorunlar, verilerin standartlaştırılması ve gereksiz verilerin elimine edilmesi gibi ihtiyaçları beraberinde getirmiştir. Her ne kadar farklı genelleştirme yöntemleri çalışılmış olsa da genelleştirmenin nasıl, ne zaman ve ne kadar etkili olduğuna dair soruların cevapları hala araştırılmaktadır. Sanal gerçeklik (SG) bilgisayar ve yazılımlar aracılığıyla kullanıcılara gerçeklik hissi veren ortamların hazırlanmasıdır. Günümüzde farklı uygulama alanları bulunan SG'den turizm alanında da yararlanılmaktadır. Turistlere bilgi amaçlı hazırlanan bu uygulamalar çoğunlukla 360 derece panorama fotoğraflarının kullanıldığı ve kullanıcı etkileşiminin oldukça kısıtlı olduğu uygulamalar şeklindedir. Bu çalışmada, hava LiDAR (Light Detection and Ranging) verileri kullanılarak Tarihi Yarımada sınırları içerisinde yer alan Eminönü Meydanı ve çevresi için turizm amaçlı LoD3 (Level of Detail) ayrıntı düzeyinde 3B kent modeli oluşturulmuştur. Çalışma üç aşamada gerçekleştirilmiştir. Birinci aşamada, LiDAR verisi sınıflandırılarak zemin, bina ve bitki örtüsü sınıfları oluşturulmuştur. İkinci aşamada, zemin ve bina sınıfları kullanılarak modelleme çalışmalarına başlanmıştır. Ayrıntılı modelin olduğu bölgede gerek zemin ve kent aksesuarları gerekse referans yapılar için ayrıntı düzeyi yüksek tutulmuştur. Çalışmanın üçüncü aşamasında ise turistik açıdan önemi olmayan binaların ayrıntı düzeyi 3B bina genelleştirme uygulanarak indirgenmiştir. Böylelikle, turistik açıdan önemli yerlerin vurgulu gösterimi mümkün olmuştur. Uygulama sırasında karşılaşılan sorunlar ve çözümleri ilgili başlıklar altında açıklanmıştır. 3B kent modelinin oluşturulması için yüksek ayrıntı düzeyindeki modeller, gerçek orto fotolar, genelleştirilmiş binalar, OpenStreetMap (OSM) ve öznitelik bilgileri gibi farklı yöntemler kullanılarak üretilmiş farklı formatlardaki verilerin oyun motoru içinde entegrasyonu sağlanmıştır. Oyun motoru tabanlı yapılan sunumda, modelin OSM ile uyumu sağlanarak kullanıcıların model içerisinde etkileşimli olarak gezerken gerçek konum ve adres bilgilerine ulaşmaları sağlanmış ve referans yapılara eklenen öznitelikler ile modelin bilgi içeriği zenginleştirilmiştir. Hazırlanan modelin geliştirilebilmesi için ilerki aşamalarda araştırılması gereken konular belirtilerek çalışma sonlandırılmıştır.

Özet (Çeviri)

The spread of three-dimensional (3D) city models has made it possible to present spatial information about cities in 3D and thus to increase the spatial perceptions of users. With the introduction of geographic data in digital environments in 3D, problems such as storing, sharing, presenting large volumes of data sets and the need for different levels of detail for different applications have brought with them the need to standardize data and eliminate unnecessary data. Although different methods of generalization have been studied, the answers to the questions of how, when and how effective generalization is still being investigated. Virtual reality (VR) is the preparation of environments that give users a sense of reality through computers and software. VR, which has different application areas, is also used in the field of tourism. Today, especially in tourism, VR technologies are used as part of the marketing strategy to provide tourists with preliminary information about hotels, cruise ships and tourist areas. Although VR is a computer-generated environment, 360-degree panorama photographs or videos have been frequently used in tourism in recent years. In this study, by using airborne LiDAR data, a 3D city model for touristic purposes at Level of Detail 3 (LoD3) is created for Eminönü Square and its surroundings within the borders of Istanbul Historical Peninsula. For the creation of the 3D city model, data in different formats produced using different methods such as models with high level of detail, real ortho photos, generalized buildings, OpenStreetMap (OSM) and attribute information are integrated into the game engine. In the game engine-based presentation, the compatibility of the model with OSM was ensured and users could access the real location and address information while interactively navigating within the model and the information content of the model was enriched with the attributes added to the reference buildings. In order to develop the prepared model, the study was terminated by specifying the issues that need to be investigated as a further work. The work is completed in three stages. In the first stage, the LiDAR point cloud was classified. For classification, macros were prepared by utilizing the multiple return (Echo) feature of LiDAR data and the point cloud was divided into three main groups: ground, vegetation and buildings. In the classification, the point cloud was grouped into a single class and the ground class was formed first by drawing points from the point cloud pool at each stage with the macros prepared. After the ground class was determined, the plant classes were divided into three subgroups as short, medium and tall using height parameters. The building class was separated from the plant class using the area parameter. In the second stage, modeling work was started using ground and building classes. In order to increase the level of detail of the model to be presented, modeling was performed in two subgroups: ground and buildings. For the ground modeling, the solid ground model of the area where the reference buildings are located was remodeled with 3D-Studio MAX software by using fewer polygons suitable for detailed overlay with reference to SAM generated from LiDAR point cloud and 1:1000 scale topographic map data. In order to increase the level of detail, high-resolution overlays obtained through field studies were used, and detailed solid ground modeling was completed by adding urban accessories. SAM was used for the ground outside the detailed area. A fully automatic method was applied to convert LiDAR points of the building class into 3D vector data. When the results are evaluated, errors such as improperly joined building corners, deformations in the ridge, improperly formed domes and awnings in front of the shops distorting the building shapes etc. were detected. In order to minimize these errors affecting the building geometry, the point data of the building class were converted into 3D building vectors by using 2D building base vector data obtained from 1:1000 scale topographic maps. Feature Manipulation Engine (FME) was used for automatic elimination of errors in building models and format conversions. Roof topology errors that could not be corrected automatically with FME were manually corrected in CityGrid (CG) software. After manual editing, buildings with LoD2 level of detail were obtained. In order to model the reference buildings as LoD3, facade photographs of the buildings were taken with a high resolution camera. Facade details such as windows, balconies, building decorations, etc. on the reference buildings were modeled using the photographs. In the area where the detailed model is located, the level of detail is kept high for both ground and urban accessories and reference buildings. With the introduction of geographic data in 3D digital environments, some problems have started to be encountered. Although different generalization methods have been studied, the answers to the questions of how, when and how effective generalization is are still under investigation.When the studies on building generalization are examined, it is seen that there are a majority of studies that consider building geometry individually or in groups. In addition to these studies that take into account building geometry, semantic-geometric, studies on machine learning, fuzzy logic and artificial intelligence have been conducted in recent years. Since there is no standard method for 3D building generalization, a method that will enable the buildings considered as reference to be highlighted from a touristic point of view has been adapted. In the last stage, the level of detail of buildings that do not have touristic importance was reduced by applying 3D building generalization. The weighted heights of the cell blocks were calculated for the generalization of the buildings at LoD2 level, which are not of touristic importance, by considering the Simple Cell Generalization, which takes into account the building floor areas and height values, and the methods in which the buildings are evaluated by dividing the buildings into two parts as bottom and top. After generalization process, the prepared building and ground models were combined in CG software based on Unity game engine and users were offered the opportunity to navigate interactively within the model. The information content of the model was increased by adding XML-based attributes to the reference structures. With the OSM integration of the model, the user is provided with location and address information within the model. It is possible to increase the level of detail to LOD4 with the integration of visuals or BIM data of the New Mosque, Spice Bazaar and other touristic places in the region. In addition, adding moving human and vehicle models and appropriate sound files to the model will increase the realism of the model by transferring the daily density of the study area to the users. The creation of avatars for users in the model and the interaction of these avatars with each other is an issue that needs to be investigated. The prepared 3D model can be packaged as exe and shared without the need for any extra software. It is possible to use the model with add-ons such as 3D virtual glasses or kinect, and it is possible to publish it on the Web via the server when necessary. The possibilities of 3DCityDB and Cesium integration should be investigated in order to map the model with large-scale attribute data and make web-based presentation. Since the model was designed for touristic purposes, the method that emphasizes reference structures by grouping building blocks in the building generalization process was applied. In addition to tourism, architectural planning, silhouette analysis, coastal management, municipal services, etc., it is necessary to investigate how Multiple Representation Databases at different levels of detail can be applied on the model to meet the needs of users from different disciplines.

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