Within the contemporary urban development discourse, the paradigm of smart cities has gained prominence over the past two decades. Ensuring sustainability in smart cities requires coherent orchestration of processes that span design, construction, operations, and management. Central to this orchestration are technologies such as Building Information Modeling (BIM), which provides detailed architectural data, and Geographic Information Systems (GIS), which provide comprehensive geographic intelligence. However, a significant challenge remains: data degradation during BIM-GIS integration. This data inconsistency, exacerbated by the different data structures of BIM and GIS, is a barrier to true interoperability. One promising solution to this conundrum is the use of Semantic Web technologies. In this study, we leverage Semantic Linked Data and geometric conversion tools to develop an algorithm that mitigates the loss of semantic information during the BIM-to-GIS conversion process. The effectiveness of this approach is underscored by a 95% accuracy rate of the converted semantic information.

Significant morphological transformations resulting from open-pit mining activities always present major problems with site safety and slope stability. This study investigates an active marble quarry in Dinar, Türkiye by combining geospatial analysis and photogrammetry based on unmanned aerial vehicles (UAV). Acquired in 2024 and 2025, high-resolution images were combined with dense point clouds produced by Structure from Motion (SfM) methods. Iterative Closest Point (ICP) registration (RMSE = 2.09 cm) and Multiscale Model-to-Model Cloud Comparison (M3C2) analysis was used to quantify the surface changes. The study found a volumetric increase of 7744.04 m³ in the dump zones accompanied by an excavation loss of 8359.72 m³, so producing a net difference of almost 615.68 m³. Surface risk factors were evaluated holistically using a variety of morphometric criteria. These measures covered surface variation in several respects: their degree of homogeneity, presence of any unevenness or texture, verticality, planarity, and linearity. Surface variation > 0.20, roughness > 0.15, and verticality > 0.25 help one to identify zones of increased instability. Point cloud modeling derived from UAVs and GIS-based spatial analysis were integrated to show that morphological anomalies are spatially correlated with possible failure zones.

Kültürel mirasa konu olan eserlerin belgelenmesinde uzun zamandır farklı yöntemler kullanılmaktadır. Sayısallaşmanın hızlı gelişimi ile ön plana çıkan yöntemlerden birisi de fotogrametri yöntemidir. Özellikle yakın resim fotogrametrisi kültürel mirasın belgelenmesinde farklı disiplinlerce etkin bir şekilde kullanılmaktadır. İnsansız hava araçları (İHA) kültürel mirasın belgelenmesinde yakın resim fotogrametrisiyle birlikte kullanılmaya başlanmıştır. İHA’ların bu alana entegre edilmesindeki en büyük etken yer merkezli çekilen fotoğraflarda üst cephelerin tam anlamıyla belgelenememesidir. Bu sorun İHA’lar ile büyük ölçüde ortadan kalkmıştır. Bu çalışmada da yakın resim fotogrametri ile İHA fotogrametrisi teknikleri kullanılarak veri füzyonu oluşturulmuş ve Kanlıdivane bazilikasının sayısal platformda fotogerçekçi doku kaplanarak 3B belgelenmesi gerçekleştirilmiştir. Çalışma sonunda elde edilen bulgular karşılaştırılmıştır. Çalışmada ayrıca elde edilen modellerin karesel ortalama hataları yersel fotogrametri, İHA fotogrametrisi ve veri füzyonu için hesaplanmış ve sırasıyla 1.306, 1.330, 1.373 cm bulunmuştur.