The “15-minute city” concept is an urban planning model that aims to ensure all essential services are accessible within a 15-minute walk or bike ride in order to reduce the reliance on cars and to offer a sustainable approach to urban living, contributing to the achievement of green and inclusive smart cities. AI (Artificial Intelligence) tools can provide the means to obtain a quick and efficient response in analysing urban geospatial data and can make data analysis a process available even for non-specialist users. Integrating AI-driven geospatial analysis in GIS can significantly enhance the planning and implementation of this concept, by providing precise, real-time data and predictive insights. The aim of this article is to analyse the possibilities of geospatial data management and analysis using AI tools, within the context of the “15-minute city”, to contribute to the process of obtaining green and inclusive smart cities.

The integration of Artificial Intelligence (AI) into remote sensing data classification has revolutionized agriculture and environmental monitoring. AI is one of the main technologies used in smart farming that enhances and optimizes the sustainability of agricultural production. The use of AI in agriculture can involve land use mapping and crop detection, crop yield monitoring, flood-prone area detection, pest disease monitoring, droughts prediction, soil content analysis and soil production capacity detection, and for monitoring the evolution of forests and vegetation. This review examines recent advancements in AI-driven classification techniques for various applications regarding agriculture and environmental monitoring to answer the following research questions: (1) What are the main problems that can be solved through incorporating AI-driven classification techniques into the field of smart agriculture and environmental monitoring? (2) What are the main methods and strategies used in this technology? (3) What type of data can be used in this regard? For this study, a systematic literature review approach was adopted, analyzing publications from Scopus and WoS (Web of Science) between 1 January 2020 and 31 December 2024. By synthesizing recent developments, this review provides valuable insights for researchers, highlighting the current trends, challenges and future research directions, in the context of achieving the Sustainable Development Goals.

Urban Digital Twins (UDTs) represent a recent application of Digital Twins (DTs), with the objective of replicating cities and providing a framework for urban planning. The utilization of UDTs provides a structured approach for the modeling and analysis of urban environments, incorporating a range of geospatial data presented in both two-dimensional (2D) and three-dimensional (3D) formats. This article details the process of processing, modeling, and integrating urban geospatial data into a Digital Twin. Two integrations for end-user platforms were demonstrated using a Geographic Information System (GIS) and an Unreal Engine (UE5) game platform. GIS-based dashboard systems provide professionals with the tools necessary to monitor, analyze, and create scenarios, thereby promoting collaboration between authorities and citizens. Game engines have the potential to play a pivotal role in the development of future UDTs by facilitating the creation of immersive experiences that aid users in comprehending their environment and promoting citizen engagement.

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.

The 15-minute city concept has emerged as a framework for promoting more sustainable, inclusive, and accessible urban environments by enabling residents to reach essential daily services within a short distance from their homes. This study evaluates potential spatial accessibility to mapped points of interest in the Berceni neighbourhood, a socialist-era residential district in southern Bucharest, Romania. Walking isochrones were generated through network analysis and subsequently indexed and aggregated using the H3 hierarchical spatial system at resolution 10. Accessibility was assessed for seven social functions using three weighting specifications: 1:1:1, 3:2:1, and 5:3:1. The final analysis included 520 mapped POIs and 8465 H3 resolution 10 cells. The results reveal broadly consistent spatial patterns across the three weighting specifications, although the weighting assumptions influence local scores, the extent of high-accessibility areas, and the relative ranking of individual H3 cells. Caring has the largest mapped opportunity count and broad spatial coverage, whereas working and governing are the least represented functions and display comparatively low potential accessibility in several parts of the neighbourhood. A function-targeted scenario involving the addition of a childcare centre in the south-western part of Berceni produces a local improvement in working accessibility but does not eliminate the wider functional deficit. The findings demonstrate the applicability of combining network-derived isochrones with H3-based spatial aggregation for neighbourhood-scale accessibility assessment.