Validation of vector data using oblique images

Abstract

Oblique images are aerial photographs taken at oblique angles to the earth's surface. Projections of vector and other geospatial data in these images depend on camera parameters, positions of the entities, surface terrain, and visibility. This paper presents a robust and scalable algorithm to detect inconsistencies in vector data using oblique images. The algorithm uses image descriptors to encode the local appearance of a geospatial entity in images. These image descriptors combine color, pixel-intensity gradients, texture, and steerable filter responses. A Support Vector Machine classifier is trained to detect image descriptors that are not consistent with underlying vector data, digital elevation maps, building models, and camera parameters. In this paper, we train the classifier on visible road segments and non-road data. Thereafter, the trained classifier detects inconsistencies in vectors, which include both occluded and misaligned road segments. The consistent road segments validate our vector, DEM, and 3-D model data for those areas while inconsistent segments point out errors. We further show that a search for descriptors that are consistent with visible road segments in the neighborhood of a misaligned road yields the desired road alignment that is consistent with pixels in the image.

Analysis of Oblique Aerial Images for Land Cover and Point Cloud Classification in an Urban Environment

“…However, most of the results utilized an orthorectified image and/or ALS data to detect buildings, vegetation, and/or ground. Meanwhile, using orthorectified images for urban object detection may induce projection errors due to relief displacement if high-rise buildings exist [15], unless a true ortho-image is utilized [24]- [26] or original imagery with interior/exterior orientation parameters is adopted [11], [15], [27], [28].…”

Automatic Orientation of Large Blocks of Oblique Images

“…The virtue of oblique photography lies in its simplicity of interpretation and understanding for inexperienced users. These qualities allowed the use of oblique images in very different applications, such as monitoring services during mass events and environmental accidents, (Petrie, 2008, Grenzdörfer et al, 2008, Kurz et al, 2007, building detection and reconstruction (Xiao et al, 2012), building structural damage classification (Nyaruhuma et al, 2012), road land updating (Mishra et al, 2008) and administration services (Lemmens et al, 2008). What is more, oblique imagery can provide a great improvement in city modelling (Wang et al, 2008) and in some cases it can serve as a good alternative to mobile mapping surveys or airborne LiDAR acquisitions.…”

Building Footprints Extraction from Oblique Imagery

“…AppleC3 / iOS Maps). Anyway, the higher completeness of the information provided by oblique images allowed their use in very different applications, such as monitoring services during mass events and environmental accidents (Petrie, 2008;Grenzdörfer et al, 2008), road land updating (Mishra et al, 2008), administration services (Lemmens et al, 2008), building detection (Xiao et al, 2012), building damages classification (Nyaruhuma et al, 2012) and city modelling (Wang, 2012). In this paper a fully automatic methodology for building footprints extraction from oblique imagery is described.…”