Latest revision as of 14:46, 21 January 2021

Abstract

Accurate, detailed and up-to-date road information is of special importance in geo-spatial databases as it is used in a variety of applications such as vehicle navigation, traffic management and advanced driver assistance systems (ADAS). The commercial road maps utilized for road navigation or the geographical information system (GIS) today are based on linear road centrelines represented in vector format with poly-lines (i.e., series of nodes and shape points, connected by segments), which present a serious lack of accuracy, contents, and completeness for their applicability at the sub-road level. For instance, the accuracy level of the present standard maps is around 5 to 20 meters. The roads/streets in the digital maps are represented as line segments rendered using different colours and widths. However, the widths of line segments do not necessarily represent the actual road widths accurately. Another problem with the existing road maps is that few precise sub-road details, such as lane markings and stop lines, are included, whereas such sub-road information is crucial for applications such as lane departure warning or lane-based vehicle navigation. Furthermore, the vast majority of roadmaps aremodelled in 2D space, whichmeans that some complex road scenes, such as overpasses and multi-level road systems, cannot be effectively represented. In addition, the lack of elevation information makes it infeasible to carry out applications such as driving simulation and 3D vehicle navigation.

Original document

The different versions of the original document can be found in:

• http://www.intechopen.com/articles/show/title/road-feature-extraction-from-high-resolution-aerial-images-based-on-image-classification-and-gab

• https://www.intechopen.com/citation-pdf-url/37525 under the license cc-by

• http://www.intechopen.com/download/pdf/37525,

http://dx.doi.org/10.5772/45893

• https://www.intechopen.com/books/remote-sensing-advanced-techniques-and-platforms/road-feature-extraction-from-high-resolution-aerial-images-based-on-image-classification-and-gab,

https://cdn.intechopen.com/pdfs/37525/InTech-Road_feature_extraction_from_high_resolution_aerial_images_upon_rural_regions_based_on_multi_resolution_image_analysis_and_gabor_filters.pdf,

https://academic.microsoft.com/#/detail/1605335821