Feasibility of Identifying and Studying the Damage of Inner-City Streets Using Drone and Satellite Images (Case Study: A Part of Yazd City)

Sustainability in the field of pavements is one of the subsets of sustainability topics in sustainable development. In the upcoming research, various supervised and object-oriented methods and fusions of satellite and drone images by using the Gram-Schmidt algorithm were used to investigate asphalt damage, including asphalt cracking and wear, in order to provide the best method for investigation. The results showed that the supervised methods of support vector machine with a kappa coefficient of 87% and overall accuracy of 90% provided the best and shortest distance method with the kappa coefficient and overall accuracy of 57% and 67%, respectively, while showing the lowest accuracy in the classification of supervised methods. Also, among the object-oriented methods, the support vector machine algorithm with a kappa coefficient of 86% and an overall accuracy of 91% had a more accurate output compared to the other studied algorithms. The lowest accuracy was related to the nearest neighbor algorithm with a kappa coefficient of 78% and an overall accuracy of 80%. In the UAV fusion output with Sentinel-2, the classification was done by using the most optimal algorithm and the support vector machine in the object-oriented method. The results showed an increase in classification accuracy up to the kappa coefficient of 91% and overall accuracy of 93%. Furthermore, the thresholding method with a Kappa coefficient of 90% showed the best result for detecting asphalt wear.