Le Wang

Individual House level Population Estimation with Remote Sensing and Artificial Intelligence

A map of a city with different colored sections based on population density.

Le Wang

Population estimates are essential for understanding and responding to many social, political, economic, and environmental problems. However, detailed and accurate population information is only available for one date per decade through the national census. With the funding of the National Science Foundation (NSF award #0822489), we developed a. suite of new methods based on Artificial Intelligence to automatically estimate population counts at individual house level with sub-meter Satellite image and Light Detection And Ranging (LiDAR). Promising results were acquired at two distinctively different cities in US: i.e. Austin, Texas (booming) and Buffalo (shrinking), NY.

Funding: National Science Foundation Award # 0822489


Wang, L., X. Li, 2017. Population Estimation with Remote Sensing. In book "Comprehensive Remote Sensing" (Editor Shunlin Liang).

Wang, L., Silvan J. 2010. "Improving small area population estimation with high resolution =mote sensing" in book "Urban Remote Sensing: Monitoring, Synthesis and Modeling in the Urban Environment", Yang X. (Editor), ISBN: 978-0-470-74958-6, Wiley-Blackwell.

Wang, L., C. Wu. 2010. Preface: Population estimation using remote sensing and GIS technologies, International Journal of Remote Sensing, 31(21):5569-5570.