Year: 2018
Type: conference-paper
Abstract: In recent years, geotagged social media has become popular as a novel source for geographic knowledge discovery. Ground-level images and videos provide a different perspective than overhead imagery and can be applied to a range of applications such as land use mapping, activity detection, pollution mapping, etc. The sparse and uneven distribution of this data presents a problem, however, for generating dense maps. We therefore investigate the problem of spatially interpolating the high-dimensional features extracted from sparse social media to enable dense labeling using standard classifiers. Further, we show how prior knowledge about region boundaries can be used to improve the interpolation through spatial morphing kernel regression. We show that an interpolate-then-classify framework can produce dense maps from sparse observations but that care must be taken in choosing the interpolation method. We also show that the spatial morphing kernel improves the results.
Authors: Xueqing Deng, Yi Zhu, Shawn Newsam
Institution: University of California, Merced
Language: English
Subfield: Geography, Planning and Development
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W2963337765