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Proceedings Paper

An algorithm to estimate building heights from Google street-view imagery using single view metrology across a representational state transfer system
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Paper Abstract

Urban ecosystem studies require monitoring, controlling and planning to analyze building density, urban density, urban planning, atmospheric modeling and land use. In urban planning, there are many methods for building height estimation using optical remote sensing images. These methods however, highly depend on sun illumination and cloud-free weather. In contrast, high resolution synthetic aperture radar provides images independent from daytime and weather conditions, although, these images rely on special hardware and expensive acquisition. Most of the biggest cities around the world have been photographed by Google street view under different conditions. Thus, thousands of images from the principal streets of a city can be accessed online. The availability of this and similar rich city imagery such as StreetSide from Microsoft, represents huge opportunities in computer vision because these images can be used as input in many applications such as 3D modeling, segmentation, recognition and stereo correspondence. This paper proposes a novel algorithm to estimate building heights using public Google Street-View imagery. The objective of this work is to obtain thousands of geo-referenced images from Google Street-View using a representational state transfer system, and estimate their average height using single view metrology. Furthermore, the resulting measurements and image metadata are used to derive a layer of heights in a Google map available online. The experimental results show that the proposed algorithm can estimate an accurate average building height map of thousands of images using Google Street-View Imagery of any city.

Paper Details

Date Published: 19 May 2016
PDF: 8 pages
Proc. SPIE 9868, Dimensional Optical Metrology and Inspection for Practical Applications V, 98680A (19 May 2016); doi: 10.1117/12.2224312
Show Author Affiliations
Elkin Díaz, Univ. Industrial de Santander (Colombia)
Henry Arguello, Univ. Industrial de Santander (Colombia)

Published in SPIE Proceedings Vol. 9868:
Dimensional Optical Metrology and Inspection for Practical Applications V
Kevin G. Harding; Song Zhang, Editor(s)

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