A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Effect of Image Classification Accuracy on Dasymetric Population Estimation
Dasymetric mapping involves the disaggregation of count data, usually relating to population/demographics, from census enumeration areas to smaller target zones with the aid of an ancillary layer related to population density. The ancillary layer is often a binary classification such as developed versus undeveloped, building versus non‐building, and residential versus non‐residential, in which one class is treated as populated and the other as unpopulated. While dasymetric mapping relies heavily on ancillary data, little research has been done to address the error in ancillary data and its effects on dasymetric mapping accuracy. This chapter reports our research effort to investigate the effect of image classification accuracy on dasymetric population estimates by developing a binomial classification of buildings from high‐resolution remote sensor imagery. The classifier was systematically and iteratively manipulated to generate a series of outputs with variegated accuracy characteristics. Lastly, we generated a corresponding series of population estimates based on the mapped building area from each iteration to investigate the relationship between the accuracy of classification and population estimation .
Effect of Image Classification Accuracy on Dasymetric Population Estimation
Dasymetric mapping involves the disaggregation of count data, usually relating to population/demographics, from census enumeration areas to smaller target zones with the aid of an ancillary layer related to population density. The ancillary layer is often a binary classification such as developed versus undeveloped, building versus non‐building, and residential versus non‐residential, in which one class is treated as populated and the other as unpopulated. While dasymetric mapping relies heavily on ancillary data, little research has been done to address the error in ancillary data and its effects on dasymetric mapping accuracy. This chapter reports our research effort to investigate the effect of image classification accuracy on dasymetric population estimates by developing a binomial classification of buildings from high‐resolution remote sensor imagery. The classifier was systematically and iteratively manipulated to generate a series of outputs with variegated accuracy characteristics. Lastly, we generated a corresponding series of population estimates based on the mapped building area from each iteration to investigate the relationship between the accuracy of classification and population estimation .
Effect of Image Classification Accuracy on Dasymetric Population Estimation
Yang, Xiaojun (editor) / McKee, Jacob (author) / Weber, Eric (author)
Urban Remote Sensing ; 283-304
2021-09-30
22 pages
Article/Chapter (Book)
Electronic Resource
English
Research Papers - Dasymetric Estimation of Population Density and Areal Interpolation of Census Data
| Online Contents | 2004
Comparison of Dasymetric Mapping Techniques for Small-Area Population Estimates
| Online Contents | 2010
Modelling the spatial distribution of Vojvodina’s population by using dasymetric method
| DOAJ | 2011