Automatic and semi-automatic methods for the extraction of topographic information are a present research topic. Especially for topographic mapping and navigation applications road extraction is important, but still not in practice. Due to the speckle effect and the limited width of roads with respect to the ground resolution automatic road extraction from SAR imagery is rather difficult. In th...

A new road classification technique from LIght Detection And Ranging (LIDAR) data is presented that relies on region growing in order to classify areas as road. The new method corrects some of the problems encountered with previously documented LIDAR road detectors. A major benefit of the new road detection method is that it can be combined with standard building detection techniques to detect ...

Avoiding vehicle-to-pedestrian crashes is a critical requirement for nowadays advanced driver assistant systems (ADAS) and future self-driving vehicles. Accordingly, detecting pedestrians from raw sensor data has a history of more than 15 years of research, with vision playing a central role. During the last years, deep learning has boosted the accuracy of image-based pedestrian detectors. Howe...

The extraction of roads from aerial and satellite images is an important task within cartography and planning of new road networks. The automation of this task is highly motivated by the expected increase of the speed and the precision of extraction. This work considers automatic road extraction from single aerial images of high resolution. It is based on two previously developed approaches: Th...

Road centerline extraction from imagery constitutes a key element in numerous geospatial applications, which has been addressed through a variety of approaches. However, most of the existing methods are not capable of dealing with challenges such as different road shapes, complex scenes, and variable resolutions. This paper presents a novel method for road centerline extraction from imagery in ...

In the recent years a number of novel, automatic map-inference techniques have been proposed, which derive road-network from a cohort of GPS traces collected by a fleet of vehicles. In spite of considerable attention, these maps are imperfect in many ways: they create an abundance of spurious connections, have poor coverage, and are visually confusing. Hence, commercial and crowd-sourced mappin...

This paper deals with the automatic detection of road network from aerial images. Our approach can be characterised by a two-level processing: firstly, a low-level knowledge is used to extract most of the roads, then semantic information is applied to solve interpretation problems. As we aim to implement this system in a production framework, we have been very careful about the reliability of t...

Developing a vision based, efficient and automatic lane detection system from a moving vehicle is a challenging task mainly due to poor quality of lane markings, occlusion created by traffic, complex road geometry and nevertheless, the non-existence of unique models. The focus of this paper is to detect line or curve like segments from a video image taken from moving vehicle and merge them to d...