Optimizing Urban Population Mapping Grid Size Selection Based on Random Forest Regression-Reference-Cited by-同舟云学术

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Optimizing Urban Population Mapping Grid Size Selection Based on Random Forest Regression

Published:2024 Issue: Volume:21 Page:1-5
ISSN:1545-598X
Container-title:IEEE Geoscience and Remote Sensing Letters
language:
Short-container-title:IEEE Geosci. Remote Sensing Lett.

Author:

Zhou Yun¹^ORCID,Gu Songwei¹,Ge Zhongxi²,Li Yuechen¹^ORCID

Affiliation:

1. Chongqing Engineering Research Center for Remote Sensing Big Data Application and the Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing, China

2. Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming, China

Funder

Research Project of Chongqing Municipal Bureau of Planning and Natural Resources

Fundamental Research Funds for the Central Universities

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/8859/10365397/10400510.pdf?arnumber=10400510

Reference16 articles.

1. Mapping Population Distribution Based on XGBoost Using Multisource Data

2. Estimating building-scale population using multi-source spatial data

3. Improved population mapping for China using remotely sensed and points-of-interest data within a random forests model

4. Mapping fine-scale population distributions at the building level by integrating multisource geospatial big data

5. Dynamic population mapping using mobile phone data

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