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; Ryan Engstrom, The George Washington University; Mariana Belgiu, Karin Pfeffer, University of Twente
Session:
WE3.R16: Urban: Urban Dynamics, Land Use, and Social Impacts IV Oral
Track:
Land Applications
Location:
Lincoln East
Presentation Time:
Wednesday, 12 August, 13:45 - 14:00
Session Chair:
Maxwell Owusu, University of Twente
Presentation
Discussion
Resources
No resources available.
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; Ryan Engstrom, The George Washington University; Mariana Belgiu, Karin Pfeffer, University of Twente
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; Wenhao Li, Shandong University of Science and Technology; Caijuan Liu, Ruiqian Zhang, Xiaogang Ning, Chinese Academy of Surveying and Mapping
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
WE3.R16.4: AN OPT3D-ASSISTED FUSION METHOD FOR MT-INSAR GEOCODING CORRECTION
Siying Li, Fengming Hu, Xiaole Ye, Jiaxin Mao, Yiming Chen, Shiyao Su, Fudan University
Resources
No resources available.