Ship detection holds great value regarding port management, logistics operations, ship security, and other crucial issues concerning surveillance and safety. Recently, ship detection from optical satellite imagery has gained popularity among the research community because optical images are easily accessible with little or no cost. However, these images’ quality and quantity of feature details are bound to their spatial resolution, which often comes in medium-low spatial resolution. Accurately detecting ships requires images with richer texture and resolution. Super-resolution is used to recover features in medium-low resolution images, which can help leverage accuracy in ship detection. In this regard, this paper quantitatively and visually investigates the effectiveness of super-resolution in enabling more accurate ship detection in medium spatial resolution images by comparing Sentinel-2A images and enhanced Sentinel-2A images. A collection of Sentinel-2A images was enhanced four times with a Real-ESRGAN model that trained PlanetScope images with high spatial resolution. Separate ship detections with YOLOv10 were implemented for Sentinel-2A images and enhanced Sentinel-2A images. The visual and metric results of both detections were compared to demonstrate the contributory effect of enhancement on the ships’ detection accuracy. Ship detection on enhanced Sentinel-2A images has a mAP50 and mAP50-95 value of 87.5% and 68.5%. These results outperformed the training process on Sentinel-2A images with a mAP value increase of 2.6% for both mAP50 and mAP50-95, demonstrating the positive contribution of super-resolution.

The utilization of three-dimensional point clouds is an advanced approach for detecting the geometry of objects within a building environment. Nonetheless, a vast amount of data still needs to be manually processed. Intelligent automation frameworks could be deployed to overcome such issues. Hence, this study proposes a machine learning-based framework for successfully classifying structural components in indoor environments. The proposed framework involves four stages: pre-processing and feature extraction, which include determining the optimum local neighborhood radius for eigenvalue-based features, feature selection, and interpretation of classification results via an explainable machine learning method. According to the proposed framework, the chi-squared test stands out for optimum local neighborhood radius determination and feature selection. The CatBoost model has the highest accuracy of 82.96%, whereas the Random Forest model’s accuracy is 82.09%. However, the training time for the Random Forest is 27 times shorter than the CatBoost. Hence, both models could be preferred to other machine learning models for practical applications due to the good balance between accuracy and calculation efficiency. Additionally, the model with the highest accuracy, CatBoost, is evaluated using the Shapley Additive exPlanations to understand the impacts of features on predictions, and according to the results, Z coordinate and verticality had a relatively high impact on the model, while others had low impacts. The proposed framework uses machine learning to classify indoor point clouds, balancing processing time and accuracy for computational efficiency in practical applications. Hence, the framework could be utilized to automate the digitalization efforts of indoor environments effectively.

Understanding the movement patterns of individuals within a structure is crucial for efficient simulation. This entails the examination of network accessibility based on insights into the intricate indoor three-dimensional network topology. The combination of Building Information Modeling with Game Engines can streamline this approach. Hence, this study proposes a pipeline integrating the A* shortest path algorithm and walkable three-dimensional navigation meshes to analyze indoor accessibility. The pipeline design was deployed in a public building, where scenario-based analyses were conducted to determine the average distance and time shifts based on blockages. According to the results, exits' positioning and availability significantly impact indoor navigation and accessibility, underscoring their significance in building design and emergency preparedness in complex buildings.

Cities are essential for economic, social, and cultural life, driving innovation but also creating inequalities. Challenges like rapid growth and inadequate infrastructure lead to vulnerabilities in urban areas. This phenomenon of social vulnerability extends beyond individual sociodemographic characteristics; it is also closely tied to the quality of services provided by the living environment, spatial organization, and accessibility. Within this framework, the phenomenon of social vulnerability is not limited to the sociodemographic characteristics of individuals; it is also directly related to the services offered by the living environment, spatial arrangements, and, in particular, accessibility. This study explores social vulnerability through the lens of accessibility and socioeconomic status, using the 15-Minute City model. The Mean Shift clustering algorithm was utilized to cluster buildings and a new classification of points of interest (POIs) with 12 categories was proposed. A theoretical accessibility analysis for these POIs were conducted for each building cluster, and an accessibility index was computed. Additionally, land cover analysis was performed for each neighborhood, followed by correlation analyses between the accessibility index, land cover, and socioeconomic scores. The findings show no significant link between land cover and accessibility, but there is a strong correlation with 0.57 between access to sports facilities and socioeconomic status. Lower socioeconomic groups have limited access to fragmented areas, while higher groups reside in well-planned regions with more services. The study shows that spatial inequalities are tied to income, living standards, density, and transportation, emphasising the need for future research on social dimensions.

Building fires are considered major disasters because of their significant effects on people, property, and the environment. This understanding has led to increased attention on developing preventive measures, particularly through the creation of effective methods for assessing fire risk. However, the effectiveness of these methods relies heavily on detailed physical and functional information of the building and data-driven decision-making. Building Information Modeling (BIM) has proven effective in representing structures, even in three dimensions. When integrated with Geographic Information Systems (GIS), it enhances spatial intelligence, leading to improved decision-making through robust multi-criteria approaches. Hence, this study develops a framework to assess fire risk in an indoor environment that deploys a BIM-based GIS and Multi-Criteria Decision-Making; this is specifically known as Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The developed framework consists of four steps: identifying fire risk parameters, calculating weights, conducting spatial fire risk assessments, and visualizing the results, where the developed concepts are tested and validated. According to the significant findings, the developed framework estimates that 18% of building rooms are at moderate risk, while the compared model identifies only 1%. This considerable difference could potentially arise from the detailed data structure of BIM and the spatial insights gained from GIS. By implementing the designed framework, key fire risk factors can be identified in three dimensions, accompanied by a comprehensive quantitative evaluation platform for fire risks within indoor environments.

The formulation of effective evacuation strategies in indoor spaces during fire incidents is paramount to minimizing loss of life. Complex structures, such as multi-storey buildings, university campuses, shopping malls, and hospitals, present unique challenges for evacuation due to their architectural designs and high occupancy levels. To address these challenges, conducting fire simulations is essential for both expediting evacuation processes and identifying the safest evacuation scenarios. However, several fragmented tools/solutions need to be integrated to model the infrastructure, simulate fire dynamics, and human behavior. Hence, this study aims to integrate these fragmented solutions into a data-driven Building Information Modeling (BIM) based platform that includes Fire Dynamics Simulator (FDS) and rule-based evacuation simulations for various human profiles. The framework analyzes various dynamic factors that can assist decision-makers in implementing strategies to mitigate and redesign structures, ultimately reducing fire risk in complex buildings. The analysis of these simulations, including congestion, accessibility, and density, enables the identification of critical factors and hazardous areas that contribute to extended evacuation times and increased smoke spread, thereby heightening the risk of loss of life.