DocumentCode :
3398466
Title :
Application of commercial remote sensing to issues in human geography
Author :
Irvine, John M. ; Kimball, Jennessa ; Regan, John ; Lepanto, Janet A.
Author_Institution :
Draper Lab., Cambridge, MA, USA
fYear :
2013
fDate :
23-25 Oct. 2013
Firstpage :
1
Lastpage :
11
Abstract :
Characterizing attributes of a society is fundamental to human geography. Cultural, social, and economic factors that are critical to understanding societal attitudes are associated with specific phenomena that are observable from overhead imagery. The application of remote sensing to specific issues, such as population estimation, agricultural analysis, and environmental monitoring, has shown great promise. Extending these concepts, we explore the potential for assessing aspects of governance, well-being, and social capital. Social science theory indicates the relationships among physical structures, institutional features, and social structures. Motivated by this underlying theory, we explore the relationship between observable physical phenomena and attributes of the society. Using imagery data from two study regions: sub-Saharan Africa and rural Afghanistan, we present an initial exploration of the direct and indirect indicators derived from the imagery. We demonstrate a methodology for extracting relevant measures from the imagery, using a combination of human-guided and machine learning methods. Our comparison of results for the two regions demonstrates the degree to which methods can generalize or must be tailored to a specific study area.
Keywords :
environmental monitoring (geophysics); geography; geophysical techniques; remote sensing; agricultural analysis; commercial remote sensing application; cultural factor; economic factor; environmental monitoring; governance aspect assessment potential; human geography issues; human-guided method; initial indirect indicator exploration; institutional features; machine learning method; observable physical phenomena; overhead imagery data; physical structures; population estimation; relevant imagery measure extraction; rural Afghanistan; social capital; social factor; social science theory; social structures; societal attitudes; society attribute characterization; specific phenomena; specific study area; sub-Saharan Africa; well-being aspect assessment potential; Agriculture; Biological system modeling; Buildings; Data models; Economics; Feature extraction; Remote sensing; economic indicators; governance; imagery; remote sensing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Applied Imagery Pattern Recognition Workshop (AIPR): Sensing for Control and Augmentation, 2013 IEEE
Conference_Location :
Washington, DC
Type :
conf
DOI :
10.1109/AIPR.2013.6749327
Filename :
6749327
Link To Document :
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