Regional Morphology and Landscape Classification using an Unsupervised Machine Learning Algorithm (Case Study: Tehran-Alborz Metropolitan Region, Iran)

In this research, regional morphology and landscape classification was done using an unsupervised machine learning algorithm (K-means clustering). In the first step, the spatial resolution (pixel size) and the set of classification indicators were determined. Finally, ۱۷ indicators were calculated in ۵*۵ km pixels using the spatial data infrastructure and spatial analysis was done in GIS software. Then, using the K-means clustering method, all the pixels of the Tehran-Alborz metropolitan region were classified (in a process based on clustering tests with ۵, ۶, and ۷ classes). Based on the obtained results, classification with ۵ classes had the best representation of regional morphology and landscape. Finally, all the results were represented spatially in the form of a morphology and landscape classification map.

کلیدواژه ها:

Regional Morphology ، Landscape ، Unsupervised Machine Learning Algorithm ، K-means Clustering ، Tehran-Alborz Metropolitan Region