The Smart Cities data and applications need to replicate, as faithfully as possible, the state of the city and to simulate possible alternative futures. In order to do this, the modelling of the city should cover all aspects of the city that are relevant to the problems that require smart solutions. In this context, 2D and 3D spatial data play a key role, in particular 3D city models. One of the methods for collecting data that can be used for developing such 3D city models is Light Detection and Ranging (LiDAR), a technology that has provided opportunities to generate large-scale 3D city models at relatively low cost. The collected data is further processed to obtain fully developed photorealistic virtual 3D city models. The goal of this research is to develop virtual 3D city model based on airborne LiDAR surveying and to analyze its applicability toward Smart Cities applications. It this paper, we present workflow that goes from data collection by LiDAR, through extract, transform, load (ETL) transformations and data processing to developing 3D virtual city model and finally discuss its future potential usage scenarios in various fields of application such as modern ICT-based urban planning and 3D cadaster. The results are presented on the case study of campus area of the University of Novi Sad.

Modern geoinformation technologies for collecting and processing data, such as laser scanning or photogrammetry, can generate point clouds with billions of points. They provide abundant information that can be used for different types of analysis. Due to its characteristics, the point cloud is often viewed as a special type of geospatial data. In order to efficiently manage such volumes of data, techniques based on a computer cluster have to be used. The Apache Spark framework has proven to be a solution for efficient processing of large volumes of data. This paper thoroughly examines the representation of point cloud data type using Apache Spark constructs. The common operations over point clouds, range queries and k-nearest neighbors queries (kNN) are implemented using Apache Spark DataFrame Application Programming Interface (API). It enabled the design of point cloud related user defined types (UDT) and user defined functions (UDF). The structure of the point cloud for efficient storing in Big Data key-value stores was analyzed and described. The methods presented in this paper were compared to PostgreSQL RDBMS, and the results were discussed.

Flood risk is increasingly shaped by the combined effects of climate change and the vulnerability of built environments, while building-level flood damage severity analysis is often constrained by limited data availability. This study develops a transparent and reproducible framework for analyzing building-level flood damage severity under climate-adjusted hazard conditions in data-constrained environments. The framework integrates administrative post-event damage records, GIS-based terrain information, a terrain-based proxy flood-depth reconstruction procedure, and a standardized Rhine Atlas/ICPR depth–damage relationship. Representative terrain-based proxy flood depths are reconstructed using building locations, terrain elevation, and settlement-level exposure assumptions. Observed damage categories are not used to assign proxy flood depths directly, but serve exclusively as empirical ordinal reference information for ordinal consistency assessment of model-derived damage severity. Climate effects are incorporated through a simplified hazard adjustment based on projected changes in extreme precipitation intensity. The framework is applied to 413 residential buildings affected by flood events in Serbia during the period 2016–2021. Results show a consistent nonlinear relationship between terrain-based proxy flood depth and ICPR-derived structural damage severity, as well as a strong influence of terrain elevation on relative hazard intensity. Climate-adjusted sensitivity scenarios indicate that even moderate increases in extreme precipitation lead to measurable increases in structural damage severity and an upward shift in model-derived damage levels. The proposed framework provides a practical approach for flood damage severity analysis in data-constrained environments, supporting improved decision-making in sustainable flood risk management and climate adaptation planning.