Changshan Wu

Changshan Wu is a Professor of Geography at the University of Wisconsin-Milwaukee (UWM), affiliated with the College of Letters & Science. His research focuses on remote sensing, GIS, spatial analysis, and their applications in urban environments, particularly in urban impervious surface analysis, population estimation, and environmental modeling. He holds a PhD in Geography from The Ohio State University (2003), an MS from the Chinese Academy of Sciences (1999), and a BS from Peking University (1995).

Education:

• PhD, Geography, The Ohio State University, 2003
• MS, Institute of Remote Sensing Applications, Chinese Academy of Sciences, 1999
• BS, Urban and Environmental Science, Peking University, China, 1995

Research Interests: Professor Wu’s work integrates remote sensing and spatial analysis to address urban and environmental challenges. Key areas include:

• Urban impervious surface estimation using spectral mixture analysis
• Small-area population interpolation with high-resolution datasets
• GIS applications in housing markets, public health, and transportation
• LiDAR-based tree crown segmentation and 3D modeling
• Environmental monitoring using multisource remote sensing data

His recent publications (2019–2024) emphasize machine learning advancements in coastal mapping, wetland classification, and global impervious surface mapping via Google Earth Engine.

Teaching: Teaches courses in remote sensing, spatial analysis, and advanced GIS modeling. Recent courses include GEOG 725 (Advanced GIS Modeling) and GEOG 750 (Remote Sensing and Urban Analysis).

Advising & Grants: While specific grants are not listed, his research has been supported by federal and institutional funding, evident through collaborations with NASA and interdisciplinary projects. Advises graduate students in GIS, remote sensing, and urban studies.

Labs/Teams: Leads UWM’s Remote Sensing and GIS Lab, focusing on urban and environmental applications. Collaborates with global institutions on projects like the global 30m impervious surface map.