The utilization of contemporary technology enhances the efficiency of parking resource management, contributing to more liveable and sustainable cities. In response to the growing challenges of urbanization, intelligent parking systems have emerged as a crucial solution for optimizing parking management, reducing traffic congestion, and minimizing pollution. The primary aim of this study is to present the concept of the developed web application that supports finding available parking spaces, embodied in the SPARK system (Smart Parking Assistance and Resource Knowledge). The integration of the YOLOv9 (You Only Look Once) segmentation algorithm with Artificial Bee Colony (ABC) optimization, combined with the use of crowdsourced data and deep learning for image analysis, significantly enhances the SPARK system’s operational efficiency. It enables rapid and precise detection of available parking spaces while ensuring robustness and continuous improvement. The accuracy of detecting available parking spaces in the presented system, estimated at 87.33%, is satisfactory compared to similar studies worldwide.

In recent years, important research has been conducted in Machine Learning (ML), especially on Artificial Neural Networks (ANN). Adaptive-Network Based Fuzzy Inference Systems (ANFIS) and Particle Swarm Optimization-Fuzzy Inference System (PSO-FIS) algorithms are popular ML algorithms like ANN. In terms of their working architecture and results, ANN, ANFIS, and PSO-FIS algorithms can obtain useful solutions for different nonlinear problems. This study evaluated the performance of the ANN, ANFIS, and PSO-FIS algorithms and compared the estimation results. Regarding the application, the test and target data was obtained from the flights performed with Unmanned Aerial Vehicles (UAV), including how long the UAV operates (i.e., Flight Time, FT) and how much battery the UAV consumes during the flight (i.e., Battery Consumption, BC). To obtain FT and BC outputs, sixty-five pre- and post-flight data tables were created. The best iterations for estimating the outputs using the three ML algorithms (considering the minimum/maximum values, RMSE, R, and R ² ) were determined and discussed based on the training, validation, and test estimations.

Optimizing vehicle placement in unmarked parking areas is essential for maximizing space efficiency, particularly in irregular and high-demand urban environments. This study investigates the optimal allocation of additional vehicles in spaces left unoccupied around parked cars by comparing seven heuristic optimization algorithms: Particle Swarm Optimization, Artificial Bee Colony, Gray Wolf Optimizer, Harris Hawks Optimizer, Phasor Particle Swarm Optimization, Multi-Population Based Differential Evolution, and the Colony-Based Search Algorithm. The experiments were conducted in two different parking areas, one designed for parallel parking and the other for perpendicular parking, under three scenarios allowing different levels of cars’ rotational flexibility. The results indicate that MDE consistently outperforms other methods in both speed and robustness, achieving the highest vehicle capacity. These findings provide a foundation for smart parking systems, enabling real-time optimization, reduced congestion, and improved urban mobility.

Prioritising properties for early review in dense urban renewal areas is a spatial multi-criteria decision problem in which physical condition, accessibility, environmental quality, and hazard exposure must be weighed before formal appraisal. This study develops a reproducible GIS-based multi-criteria decision analysis workflow for pre-transformation valuation support in Fatih Neighborhood, Esenler District, Istanbul, covering 54 independent property units. A 311-indicator geodatabase was screened for local discrimination, leaving 284 active sub-criteria under 10 retained main criteria. Expert pairwise judgments were converted into weights by six procedures compared under a common log-ratio objective, and the resulting TOPSIS rankings were tested for redundancy, benchmark divergence, judgment uncertainty, and structural robustness. The retained variables carry substantial overlapping spatial information, so the matrix does not provide hundreds of independent evidence signals, and equal within-group allocation concentrates influence in single-variable criteria. The leading priority tier nevertheless remains stable under calibrated weight variation and realistic judgment noise, whereas the wider rank order is sensitive to grouping, weighting, normalisation, and scoring choices. The workflow therefore separates robust priority signals from model-dependent rankings without interpreting the scores as market-value estimates, so that the intensity of field verification and appraisal can follow the reported stability of each unit.

The digital preservation of built cultural heritage requires precise documentation techniques capable of capturing complex architectural geometries often affected by occlusions and data voids. This study presents a robust multi-sensor fusion workflow integrating Terrestrial Laser Scanning (TLS) and Unmanned Aerial Vehicle (UAV) photogrammetry for the 3D reconstruction of the Hasaköy (Sasima) Church in Niğde, Türkiye. To address the limitations of traditional registration methods, specifically the susceptibility of the Iterative Closest Point (ICP) algorithm to local minima in datasets with partial overlaps, this study proposes a fine-tuning approach based on the Multi-population Based Differential Evolution (MDE) algorithm. The methodology employs a coarse-to-fine strategy, initiating with Fast Point Feature Histogram (FPFH) extraction and RANSAC (Random Sample Consensus) for global alignment, followed by TR-ICP, MDE, PSO, and Aquila Optimizer (AO) evaluation, computational-time analysis, FPFH-radius sensitivity testing, and 6-DoF transformation decomposition to characterize both accuracy and operational cost. In the 30-run fine-tuning evaluation, MDE reduced the mean bidirectional trimmed RMSE from 0.4152 m for TR-ICP to 0.3726 m. With a population parameter of 10, MDE retained a low median RMSE of 0.3718 m, while PSO exhibited a wider stochastic tail under the same bounded 6-DoF search budget. AO produced a higher mean bidirectional trimmed RMSE of 0.5233 m. The decimeter-scale bidirectional RMSE should be interpreted as a cross-source, partial-overlap distance metric rather than sensor precision; the overlapping facade objective was approximately 2.4–2.8 cm, and the UAV block was independently controlled with a 1.34 cm GCP RMSE. This study establishes a transparent and reproducible framework for heritage documentation, supporting the faithful digital preservation of endangered monuments with complex typologies.