WE3.R16.1
Mapping deprived areas in a heterogeneous urban environment using a machine learning density cluster approach
Maxwell Owusu, Monika Kuffer, University of Twente, Netherlands; Ryan Engstrom, The George Washington University, United States; Mariana Belgiu, Karin Pfeffer, University of Twente, Netherlands
Session:
WE3.R16: Urban: Urban Dynamics, Land Use, and Social Impacts IV Oral
Track:
Land Applications
Location:
TBD
Presentation Time:
Wed, 12 Aug, 13:45 - 14:00
Presentation
Discussion
Resources
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Session WE3.R16
WE3.R16.1: Mapping deprived areas in a heterogeneous urban environment using a machine learning density cluster approach
Maxwell Owusu, Monika Kuffer, University of Twente, Netherlands; Ryan Engstrom, The George Washington University, United States; Mariana Belgiu, Karin Pfeffer, University of Twente, Netherlands
WE3.R16.2: A Deep Learning–Based Approach for Parcel-Level Inefficient Land Identification Using High-Resolution Remote Sensing: A Case Study of Jinan
Jinhong Yu, Hao Wang, Chinese Academy of Surveying and Mapping, China; Wenhao Li, Shandong University of Science and Technology, China; Caijuan Liu, Ruiqian Zhang, Xiaogang Ning, Chinese Academy of Surveying and Mapping, China
WE3.R16.3: MACHINE LEARNING DRIVEN MULTI-SENSOR AND BUILT-FORM FUSION USING SELF-ORGANIZING MAPS FOR URBAN ENVIRONMENTAL STRESS MAPPING IN LAHORE
Asfra Rizwan Toor, Hajra Javed, Zubair Khalid, Lahore University of Management Sciences, Pakistan
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