This study aims to bridge the fields of urban morphology and land use/land cover (LULC) mapping through a systematic analysis of their integration in recent research. The research employs systematic literature review (SLR) methodology combining quantitative and qualitative methods through four methodological steps: data search, data selection, data analysis, and data clustering. The analysis performed three distinct clustering patterns: (1) methods and tools, (2) data types, and (3) urban morphology aspects. The results reveal five distinct methodological approaches—Data-Driven Typological Decoding Approach, Quantitative Structural Metrics Approach, Predictive Spatiotemporal Transition Approach, Temporal Change Detection and Performance Approach, and Spatial Configuration and Density Analysis Approach—each contributing unique insights to urban form analysis. The findings demonstrate the multidimensional nature of urban form analysis, incorporating both social and temporal dimensions, while highlighting the essential role of change detection in understanding urban pattern evolution. This systematic review establishes a comprehensive framework for understanding the relationship between urban morphology and LULC mapping, providing valuable insights for future research integration.

This study investigates the intersection of digital tools and methods with the built environment disciplinary framework, focusing on Urban Planning and Development (UPD), Architecture, Engineering, and Construction (AEC), and Cultural Heritage (CH) domains. Using a systematic literature review of 29 solution-oriented documents, the research applies both bibliometric and in-depth content analysis to identify methodological patterns. Co-occurrence mapping revealed four thematic clusters—Data Integration and User-Centric Analysis, Advanced 3D Spatial Analysis and Processing, Real-Time Interaction and Digital Twin Support, and 3D Visualization—each corresponding to distinct stages in a digital workflow, from data acquisition to interactive communication. Comparative and interdependency analyses demonstrated that these clusters operate in a sequential yet interconnected manner, with Data Integration forming the foundation for analysis, simulation, and visualization tasks. While current solutions are robust within individual stages, they remain fragmented, indicating a need for systemic interoperability. The findings underscore the opportunity to develop integrated digital platforms that synthesize these clusters, enabling more comprehensive observation, management, and planning of the built environment. Such integration could strengthen decision-making frameworks, enhance public participation, and advance sustainable, smart city development.

Two important tasks in many e-commerce applications are identity verification of the user accessing the system and determining the level of rights that the user has for accessing and manipulating system’s resources. The performance of these tasks is directly dependent on the certainty of establishing the identity of the user. The main research focus of this paper is user identity verification approach based on voice recognition techniques. The paper presents research results connected to the usage of open-source speaker recognition technologies in e-commerce applications with an emphasis on evaluating the performance of the algorithms they use. Four open-source speaker recognition solutions (SPEAR, MARF, ALIZE, and HTK) have been evaluated in cases of mismatched conditions during training and recognition phases. In practice, mismatched conditions are influenced by various lengths of spoken sentences, different types of recording devices, and the usage of different languages in training and recognition phases. All tests conducted in this research were performed in laboratory conditions using the specially designed framework for multimodal biometrics. The obtained results show consistency with the findings of recent research which proves that i-vectors and solutions based on probabilistic linear discriminant analysis (PLDA) continue to be the dominant speaker recognition approaches for text-independent tasks.

The study explores the emerging intersection of urban morphology and digital technologies, focusing on the methods and tools employed to analyze and interpret urban patterns. With rapid urbanization and the resultant complex urban forms, understanding these patterns is crucial for sustainable city planning and design. This contribution presents the findings from the initial deliverable of the SPATTERN project which aims to advance environmentally sensitive urban development of spa settlements in Serbia through the affirmation of twin transition. First SPATTERN deliverable provides a comprehensive review of existing methodological approaches by synthesizing insights from an extensive literature review and content analysis, alongside showcasing relevant tools and methods. The research is performed through systematic literature review combined with the visualization of bibliometric networks and dictionary design and development through 4-cycle research protocol: (1) data search and selection, (2) data clustering and co-occurrence, (3) dictionary development, and (4) factsheets analysis. The review encompasses an extensive analysis of selected terms (keywords) highlighting how digital tools facilitate the collection, visualization, and analysis of urban data. Moreover, the review addresses the challenges and limitations associated with these methods, such as data quality, integration issues, and the need for interdisciplinary collaboration. This dictionary not only standardizes the terminology but also provides a conceptual map for researchers and practitioners navigating the complex nature of urban pattern analysis. By bridging the gap between traditional approaches for urban analysis and cutting-edge digital approaches, the findings advocate for a more informed and systematic use of digital tools and methods in urban morphology, thus promoting more resilient and adaptable urban environments.