Change detection has a history spanning over four decades in military and civilian applications. Monitoring and controlling urban changes, particularly identifying unauthorized land-use changes, is essential for urban management. Traditional methods lack efficiency due to limitations in accuracy, speed, and comprehensiveness, while newer deep learning approaches face challenges like training data preparation, time-consuming processes, and high computational demands. This paper proposes a relatively fast, low-cost, and high-accuracy method using photogrammetric products with planar and vertical accuracies better than 30 cm. By applying thresholds and filters, a Digital Difference Model (DDM) is generated to detect changes in residential areas. Overall accuracy in two test sites exceeded 90% and 83%, respectively. Disturbing features were removed by masking Orthophotomosaics using intelligent algorithms. Applying optimized filters in four stages improved accuracy by over 30%. While the process depends on regional characteristics and urban-specific thresholds, its lower cost and higher speed make it widely applicable to similar areas. For regions with different urban textures, optimal thresholds and parameters must be recalculated using the same methodology.