Next Article in Journal
Mapping Vegetation at Species Level with High-Resolution Multispectral and Lidar Data Over a Large Spatial Area: A Case Study with Kudzu
Next Article in Special Issue
An Optimal Population Modeling Approach Using Geographically Weighted Regression Based on High-Resolution Remote Sensing Data: A Case Study in Dhaka City, Bangladesh
Previous Article in Journal
ScienceEarth: A Big Data Platform for Remote Sensing Data Processing
Previous Article in Special Issue
Estimating Fine-Scale Heat Vulnerability in Beijing Through Two Approaches: Spatial Patterns, Similarities, and Divergence
Article

Mapping Fine-Scale Urban Spatial Population Distribution Based on High-Resolution Stereo Pair Images, Points of Interest, and Land Cover Data

by 1, 1,* and 2,3,4,5
1
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
2
School of Resources and Environmental Science, Wuhan University, Wuhan 430072, China
3
Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China
4
International Initiative on Spatial Life course Epidemiology (ISLE), Hong Kong, China
5
Faculty of Geo-Information Science and Earth Observation, University of Twente, 7514 AE Enschede, The Netherlands
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(4), 608; https://doi.org/10.3390/rs12040608
Received: 26 December 2019 / Revised: 22 January 2020 / Accepted: 30 January 2020 / Published: 12 February 2020
Fine-scale population distribution is increasingly becoming a research hotspot owing to its high demand in many applied fields. It is of great significance in urban emergency response, disaster assessment, resource allocation, urban planning, market research, and transportation route design. This study employed land cover, building address, and housing price data, and high-resolution stereo pair remote sensing images to simulate fine-scale urban population distribution. We firstly extracted the residential zones on the basis of land cover and Google Earth data, combined them with building information including address and price. Then, we employed the stereo pair analysis method to obtain the building height on the basis of ZY3-02 high-resolution satellite data and transform the building height into building floors. After that, we built a sophisticated, high spatial resolution model of population density. Finally, we evaluated the accuracy of the model using the survey data from 12 communities in the study area. Results demonstrated that the proposed model for spatial fine-scale urban population products yielded more accurate small-area population estimation relative to high-resolution gridded population surface (HGPS). The approach proposed in this study holds potential to improve the precision and automation of high-resolution population estimation. View Full-Text
Keywords: urban population; stereo pair image; geospatial technique; points of interest; fine-scale population urban population; stereo pair image; geospatial technique; points of interest; fine-scale population
Show Figures

Graphical abstract

MDPI and ACS Style

Xu, M.; Cao, C.; Jia, P. Mapping Fine-Scale Urban Spatial Population Distribution Based on High-Resolution Stereo Pair Images, Points of Interest, and Land Cover Data. Remote Sens. 2020, 12, 608. https://doi.org/10.3390/rs12040608

AMA Style

Xu M, Cao C, Jia P. Mapping Fine-Scale Urban Spatial Population Distribution Based on High-Resolution Stereo Pair Images, Points of Interest, and Land Cover Data. Remote Sensing. 2020; 12(4):608. https://doi.org/10.3390/rs12040608

Chicago/Turabian Style

Xu, Min, Chunxiang Cao, and Peng Jia. 2020. "Mapping Fine-Scale Urban Spatial Population Distribution Based on High-Resolution Stereo Pair Images, Points of Interest, and Land Cover Data" Remote Sensing 12, no. 4: 608. https://doi.org/10.3390/rs12040608

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop