Whenever natural and human-made disasters strike, the proper response of the concerned authorities often relies on search and rescue services. Search and rescue services are complex multidisciplinary processes that involve several degrees of interdependent assignments. To handle such complexity, decision support systems are used for decision-making and execution of plans within search and rescue operations. Advances in data management solutions and artificial intelligence technologies have provided better opportunities to make more efficient and effective decisions that can lead to improved search and rescue operations. This paper provides findings from a bibliometric mapping and a systematic literature review performed to: (1) identify existing search and rescue processes that use decision support systems, data management solutions, and artificial intelligence technologies; (2) do a comprehensive analysis of existing solutions in terms of their research contributions to the investigated domain; and (3) investigate the potential for knowledge transfer between application areas. The main findings of this review are that non-conventional data management solutions are commonly used in land rescue operations and that geographical information systems have been integrated with various machine learning approaches for land rescue. However, there is a gap in the existing research on search and rescue decision support at sea, which can motivate future studies within this specific application area.

Urban management systems evolve from merely overseeing urban growth to comprehending interactions among cities’ natural, physical, cyber, and social systems. Urban digital twins leverage emerging technologies and serve as platforms to support city transitions. However, a generic urban digital twin framework with essential building blocks leading to a sustainable urban digital transformation is lacking, as most urban digital twin implementations focus on specific application areas and usages. In addition, as urban digital twins are rather new developments, it is important to clarify their concept, definition, and characteristics, including their joint components and functions. This paper presents findings from a domain analysis based on a systematic literature review on urban digital twins, including investigating the concept, definition, and characteristics of urban digital twins and identifying joint components and functions as building blocks for developing a conceptual framework of urban digital twins for sustainable digital transformation. In total, we identified six main components that could be subdivided into 16 components. Moreover, we identified four main functions that could be subdivided into ten functions. A conceptual framework for urban digital twins is proposed based on those components and functions. Two case studies were conducted to illustrate the framework’s applicability. It is an overarching goal that the proposed framework will become a useful reference and decision-making tool for urban planners, designers, constructors, and others.

The analysis of individuals’ movement behaviors is an important area of research in geographic information sciences, with broad applications in smart mobility and transportation systems. Recent advances in information and communication technologies have enabled the collection of vast amounts of mobility data for investigating movement behaviors using trajectory data mining techniques. Trajectory clustering is one commonly used method, but most existing methods require a complete similarity matrix to quantify the similarities among users’ trajectories in the dataset. This creates a significant computational overhead for large datasets with many user trajectories. To address this complexity, an efficient clustering-based method for network constraint trajectories is proposed, which can help with transportation planning and reduce traffic congestion on roads. The proposed algorithm is based on spatiotemporal buffering and overlapping operations and involves the following steps: (i) Trajectory preprocessing, which uses an efficient map-matching algorithm to match trajectory points to the road network. (ii) Trajectory segmentation, where a Compressed Linear Reference (CLR) technique is used to convert the discrete 3D trajectories to 2D CLR space. (iii) Spatiotemporal proximity analysis, which calculates a partial similarity matrix using the Longest Common Subsequence similarity indicator in CLR space. (iv) Trajectory clustering, which uses density-based and hierarchical clustering approaches to cluster the trajectories. To verify the proposed clustering-based method, a case study is carried out using real trajectories from the GeoLife project of Microsoft Research Asia. The case study results demonstrate the effectiveness and efficiency of the proposed method compared with other state-of-the-art clustering-based methods.

The need for effective and efficient search and rescue operations is more important than ever as the frequency and severity of disasters increase due to the escalating effects of climate change. Recognizing the value of personal knowledge and past experiences of experts, in this paper, we present findings of an investigation of how past knowledge and experts’ experiences can be effectively integrated with current search and rescue practices to improve rescue planning and resource allocation. A special focus is on investigating and demonstrating the potential associated with integrating knowledge graphs and case-based reasoning as a viable approach for search and rescue decision support. As part of our investigation, we have implemented a demonstrator system using a Norwegian search and rescue dataset and case-based and concept-based similarity retrieval. The main contribution of the paper is insight into how case-based and concept-based retrieval services can be designed to improve the effectiveness of search and rescue planning. To evaluate the validity of ranked cases in terms of how they align with the existing knowledge and insights of search and rescue experts, we use evaluation measures such as precision and recall. In our evaluation, we observed that attributes, such as the rescue operation type, have high precision, while the precision associated with the objects involved is relatively low. Central findings from our evaluation process are that knowledge-based creation, as well as case- and concept-based similarity retrieval services, can be beneficial in optimizing search and rescue planning time and allocating appropriate resources according to search and rescue incident descriptions.

Reducing light pollution and promoting sustainable lighting practices require new tools that integrate environmental, social, and technical considerations in design processes. Urban Digital Twins are emerging as powerful instruments for this purpose, enabling integrated evaluation of lighting impacts on both people and ecosystems. This paper presents an exploratory evaluation of NorDark-DT, a recently developed urban digital twin designed to support the sustainable planning of lighting infrastructure in green urban areas. This exploratory assessment was conducted with master’s students engaged in lighting design practices. Participants performed two task-oriented exercises of planning and comparing lighting configurations after-dark for a site in Uppsala, Sweden. Results show that NorDark-DT effectively facilitates the exploration of alternative lighting solutions within realistic green urban area contexts and encourages reflection on issues such as light pollution, biodiversity, and ecological preservation. Nevertheless, further improvements are required to enhance the user interface, expand analytical capabilities, strengthen integration with professional lighting software, and optimize performance for varying hardware setups. Beyond professional practice, the tool also proved valuable for educational purposes by promoting interdisciplinary collaboration and broadening students’ understanding of sustainability in lighting design. Overall, this study provides an initial step in a usability assessment of NorDark-DT, confirming its potential to support environmentally responsible, socially aware, and well-informed lighting interventions.