Prof.  Mahmoud Reza  Delavar
University of Tehran,  Iran

Title: Smart Remote Sensing and GIS Data Fusion for Urban Growth Modeling

Abstract:

Biography:

Mahmoud Reza Delavar has obtained a BSc. in Civil Eng.-Surveying from KNT University, Iran in 1988, a MSc. in Civil Eng.-Photogrammetry and Remote Sensing from University of Roorkee (IIT Roorkee), India in 1992 and a PhD in Geomatic Eng.-GIS from UNSW, Australia in 1997. Dr. Delavar is Professor in GIS and Director, Center of Excellence in Geomatic Eng. in Disaster Management, and Director, Land Administration in Smart City Lab., School of Surveying and Geospatial Eng., College of Engineering, University of Tehran. In addition, Prof. Delavar is Director of Smart Disaster Management Research Center which is a network of Iranian University Professors in Disaster Management. Dr. Delavar is a member of Supreme Council of Earthquake Prediction Center, Iran’s International Institute of Earthquake Engineering and Seismology. He is the national representative to the International Society of Urban Data Management (UDMS) since 2006, chaired ISPRS WG IV/3 (Spatial data analysis, statistics and uncertainty modeling), 2016-2022 and chairing ISPRS WG IV/2 (Artificial Intelligence and Uncertainty Modeling in Spatial Analysis), 2022-2026. Prof. Delavar is a member of the Expert Committee of China International Association for Promotion of Science and Technology International Academic Exchange Working Committee (Dec. 6, 2022-Dec. 5, 2025). Dr. Delavar is in the editorial board of ISPRS International Journal of Geo-Information (IJGI) and International Journal of Geo-spatial Information Science (GSIS) and published 390 papers in reputable national and international conferences and research Journals. Prof. Delavar has supervised 114 MSc., PhD and Postdoc research so far. His research interests are in spatial data quality and uncertainty modeling, temporal GIS, disaster management, smart cities, cadaster, land administration, SDI, digital terrain modeling, spatial data fusion, spatial data science, spatial big data, land use and land cover change detection and urban growth modeling.