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TitleSynoptic mapping of high-rise buildings in urban areas based on combined shadow analysis and scale space processing
AuthorZhang, Y; Guindon, B
SourceInternational Journal of Remote Sensing vol. 35, no. 5, 2014 p. 367-381, https://doi.org/10.1080/01431161.2014.902549
Year2014
Alt SeriesEarth Sciences Sector, Contribution Series 20130503
PublisherInforma UK Limited
Documentserial
Lang.English
Mediapaper; on-line; digital
File formatpdf
Subjectsgeophysics; remote sensing; urban geology; urban planning; satellite imagery
Illustrationslocation maps; flow charts; satellite images
ProgramMethodology, Remote Sensing Science
Released2014 04 29
AbstractA highly automated methodology is described to map locations and heights of highrise buildings from single high-resolution multi-spectral satellite imagery. The approach involves preliminary shadow detection using the Tsai colour invariant transform and scale space processing to identify candidate building pixels. Application of shadow-building and shadow length constraints led to mapping of the location and height of building candidate objects. The approach has been applied to a winter SPOT 5 scene of Beijing, China. Tests of buildings in a suburban area indicate that a high detection rate (93%) can be achieved for buildings taller than 28 m. A height estimation accuracy of 20 m has also been met for these buildings.
Summary(Plain Language Summary, not published)
Urban roughness, or the variations in surface height over an urbanized area, is a key input to urban meteorological models. While urban roughness information such as building density and height can be assembled from city plans or airborne overflights, these approaches are costly and/or are not practical for many rapidly growing cities in developing countries. This paper describes an automated methodology developed to detect tall buildings and estimate their heights in urban areas using high resolution (or high detail) satellite imagery. The methodology was tested using imagery of a suburban area of Beijing, China, and results show a detection rate of 93% for tall buildings.
GEOSCAN ID293795