Evaluating the Relationship Between Contextual Features Derived from Very High Spatial Resolution Imagery and Urban Attributes: A Case Study in Sri Lanka

被引:0
|
作者
Engstrom, Ryan [1 ]
Harrison, Robert [1 ]
Mann, Michael [1 ]
Fletcher, Amanda [1 ]
机构
[1] George Washington Univ, Dept Geog, Washington, DC 20052 USA
关键词
Machine Learning; Contextual Features; Buildings; Roads;
D O I
10.1109/jurse.2019.8809041
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Extracting information about variations within urban areas using satellite imagery has generally focused on mapping individual buildings or slum versus non-slum areas. While these data are useful, they can run into issues in very dense urban areas, additionally slums have a subjective definition. In previous research we have found that contextual features are related to population, census variables, poverty, and other values, but have not explored which urban attributes (i.e., buildings and roads) these features represent. In this study we seek to determine the correlation between contextual features calculated on Very High Spatial Resolution (VHSR) satellite data and urban attributes derived from Open Street Map (OSM) for portions of multiple cities in Sri Lanka. Results indicate that individual contextual features are highly correlated with building area, building density, road area, road density, total built up areas and other features. Moreover, when multiple contextual features are combined within a model, they can explain from 70 to 92 percent of the variance of these urban features within the study area. This indicates that contextual features are very strong indicators of urban variability and can be used to map differences within the urban setting. This may allow us to forgo having to map each building and road individually for mapping urban areas in future projects.
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页数:4
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