Regional Morphology and Landscape Classification using an Unsupervised Machine Learning Algorithm (Case Study: Tehran-Alborz Metropolitan Region, Iran)
Publish place: The third international congress of civil engineering, architecture, building materials and environment
Publish Year: 1402
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
CAUCONG03_155
تاریخ نمایه سازی: 16 اردیبهشت 1403
Abstract:
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.
Keywords:
Regional Morphology , Landscape , Unsupervised Machine Learning Algorithm , K-means Clustering , Tehran-Alborz Metropolitan Region
Authors
Ebrahim Zargari-Marandi
Ph.D. in Urban Planning, University of Tehran; Tehran; Director of Urban and Regional Plans, Urban Planning & Architecture Research Center of Iran [UARC], Tehran, Iran
Mohammad-Saleh Arabahmadi
GIS Manager at Shahrig engineering Co. Ltd.