Validation of Vector Data using Oblique Images

TL;DR

This paper introduces a scalable algorithm that uses image descriptors and machine learning to detect and correct inconsistencies in vector geospatial data from oblique aerial images, improving data validation.

Contribution

It presents a novel method combining image descriptors and SVM classification to identify and correct vector data errors using oblique images.

Findings

Successfully detects occluded and misaligned road segments

Validates vector, DEM, and 3D model data

Improves accuracy of geospatial data validation

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 geospatial entities, surface terrain, occlusions, 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.