Smart cities use digital technologies such as cloud computing, Internet of Things, or open data in order to overcome limitations of traditional representation and exchange of geospatial data. This concept ensures a significant increase in the use of data to establish new services that contribute to better sustainable development and monitoring of all phenomena that occur in urban areas. The use of the modern geoinformation technologies, such as sensors for collecting different geospatial and related data, requires adequate storage options for further data analysis. In this paper, we suggest the biG dAta sMart cIty maNagEment SyStem (GAMINESS) that is based on the Apache Spark big data framework. The model of the GAMINESS management system is based on the principles of the big data modeling, which differs greatly from standard databases. This approach provides the ability to store and manage huge amounts of structured, semi-structured, and unstructured data in real time. System performance is increasing to a higher level by using the process parallelization explained through the five V principles of the big data paradigm. The existing solutions based on the five V principles are focused only on the data visualization, not the data themselves. Such solutions are often limited by different storage mechanisms and by the ability to perform complex analyses on large amounts of data with expected performance. The GAMINESS management system overcomes these disadvantages by conversion of smart city data to a big data structure without limitations related to data formats or use standards. The suggested model contains two components: a geospatial component and a sensor component that are based on the CityGML and the SensorThings standards. The developed model has the ability to exchange data regardless of the used standard or the data format into proposed Apache Spark data framework schema. The verification of the proposed model is done within the case study for the part of the city 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.

Effective planning, management, and optimization of electrical distribution networks are essential components of modern smart city development and energy sustainability. This study presents a geospatial decision-support system for the low and medium voltage (LV/MV) network of Elektrokrajina d.o.o., the main electricity distributor in Republika Srpska. Model of the system is built upon the extended INSPIRE Utility Network – Electricity model. It leverages a 2D/3D WebGIS platform (ELMAP) to visualize, analyze, and manage electrical infrastructure using open-source technologies such as PostgreSQL/PostGIS, pgRouting, Geoserver and Mapbox. The integration of real-time and semi-structured data from sensors and enterprise systems (e.g., SAP, MDM) is enabled via service-oriented architecture and parallelized query execution. Particular attention is given to addressing topological inconsistencies in legacy data, reconstructing network topology, and linking meter positions to buildings using GPS and OSM data. Custom algorithms were developed for voltage-level tracking, meter reading route optimization, and network loss analysis, including indicators such as SAIFI, SAIDI, and peak loads. The platform enables spatial identification of critical infrastructure points and supports strategic decisions regarding transformer zoning, network upgrades, and demand management. This research highlights the importance of structured 3D geospatial data and demonstrates how scalable, open-source WebGIS systems can support the energy sector in meeting smart grid and sustainability goals.

The need to produce organic food and increase yields plays a significant role in the planning of agricultural production and the economy of the state in general. Monitoring and modeling of all stages of production implies the establishment of smart agriculture concepts based on the use of remote sensing results. This work procedure implies abandoning the classic homogenization in the approach to the cultivation of agricultural land and provides the possibility of anticipating problems and timely action, which should provide an increase in yield with environmental production conditions.

Thank you for the opportunity to review this article. Before publication, you need to make some improvements and clarifications. You need to explain the methodological part in more detail. Also, the part of related works needs to be defined accordi...