Urban areas have developed organically over time, driven by the economic success of cities. However, this development has usually been accompanied by the side effects of urbanization, such as increased traffic and its associated problems: traffic congestion, increased accident rates and pollution. As urban populations grow and expand, the importance of GIS lies in its ability to collect a large amount of geospatial data, including human-generated data. This data is necessary to understand the complexity of the city, set priorities, solve complicated planning problems and perform a variety of spatial analysis, which shows not only the feasibility but also the consistency of the proposed infrastructure with the requirements of a sustainable city. In this paper, we demonstrate the benefits of integrating real-time traffic data with GIS technology and remote sensing data for analyzing the impact of infrastructure works and COVID-19 on traffic in Oradea, Romania. The case study was focused on the historical center of Oradea and was based on remote sensing data collected before, during, and after traffic restrictions. The study also shows the need for using GIS and crowdsourcing-based applications in traffic analysis and planning.

The degree of urbanization and the uncontrolled expansion of the built environment play a defining role in shaping contemporary society, contributing significantly to abrupt temperature fluctuations and a declining quality of life. This study aims to analyze land use and land cover (LULC) patterns in the municipality of Deva, located in the central part of Hunedoara County, Romania (45°52′ N, 22°54′ E). The analysis covers the period from March 2022 to March 2023 and is based on open-source datasets. Supervised classification of LULC was performed using two GIS software platforms: ArcGIS Pro and QGIS. Sentinel-2A satellite imagery, with spatial resolutions of 10 m, 20 m, and 60 m, was processed using two different classification algorithms—the Minimum Distance classifier (via the Semi-Automatic Classification Plugin in QGIS) and the k-Nearest Neighbor (k-NN) algorithm in ArcGIS Pro. The comparative accuracy assessment indicated that the k-NN classifier in ArcGIS Pro performed better, achieving an overall accuracy of 89.7% and a Kappa coefficient of 0.86, while the Minimum Distance classifier in QGIS obtained an overall accuracy of 81.2% and a Kappa coefficient of 0.78. The outputs of both classification workflows were compared, and an accuracy assessment was conducted during the post-processing stage. The best results were obtained using the k-NN algorithm. The classification maps generated in this study can serve as a valuable foundation for local authorities to monitor environmental changes and support urban planning initiatives in Deva.

The Historical Centre of Bucharest City is a highly populated area and a popular tourist attraction, known for its old buildings, some of which were built before the year 1900 and were severely damaged by the 7.4 magnitude earthquake in 1977. Seismic risk available data consist of lists published by the Municipal Administration for the Consolidation of Buildings with Seismic Risk, and include various information about the buildings for which technical expertise was conducted. Available cadastral data can provide additional information about the buildings, including the buildings’ location and area. In this article, we aim to integrate the available cadastral data with seismic risk data in an online building database for the Historical Centre of Bucharest City. Such a database can be used for further 3D city modelling, which can be helpful in disaster management and emergency situations response planning. Previous initiatives in this regard did not provide precise geospatial data or important attributes regarding the situation of these buildings. It is vital to take into account the official data related to the property, mentioned in the National Land Registry.