Article ID Journal Published Year Pages File Type
6937642 Computer Vision and Image Understanding 2016 13 Pages PDF
Abstract
We address the task of estimating large-scale land surface conditions using overhead aerial (macro-level) images and street view (micro-level) images. These two types of images are captured from orthogonal viewpoints and have different resolutions, thus conveying very different types of information that can be used in a complementary way. Moreover, their integration is necessary to enable an accurate understanding of changes in natural phenomena over massive city-scale landscapes. The key technical challenge is devising a method to integrate these two disparate types of image data in an effective manner, to leverage the wide coverage capabilities of macro-level images and detailed resolution of micro-level images. The strategy proposed in this work uses macro-level imaging to learn the extent to which the land condition corresponds between land regions that share similar visual characteristics (e.g., mountains, streets, buildings, rivers), whereas micro-level images are used to acquire high resolution statistics of land conditions (e.g., the amount of debris on the ground). By combining macro- and micro-level information about regional correspondences and surface conditions, our proposed method is capable of generating detailed estimates of land surface conditions over an entire city.
Keywords
Related Topics
Physical Sciences and Engineering Computer Science Computer Vision and Pattern Recognition
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