Automatic road network extraction in suburban areas from aerial images

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Challenges in Partially-Automated Roadway Feature Mapping Using Mobile Laser Scanning and Vehicle Trajectory Data

Connected vehicle and driver's assistance applications are greatly facilitated by Enhanced Digital Maps (EDMs) that represent roadway features (e.g., lane edges or centerlines, stop bars). Due to the large number of signalized intersections and miles of roadway, manual development of EDMs on a global basis is not feasible. Mobile Terrestrial Laser Scanning (MTLS) is the preferred data acquisition method to provide data for automated EDM development. Such systems provide an MTLS trajectory and a point cloud for the roadway environment. The challenge is to automatically convert these data into an EDM. This article presents a new processing and feature extraction method, experimental demonstration providing SAE-J2735 map messages for eleven example intersections, and a discussion of the results that points out remaining challenges and suggests directions for future research.Comment: 6 pages, 5 figure

Road Feature Extraction from High Resolution Aerial Images Upon Rural Regions Based on Multi-Resolution Image Analysis and Gabor Filters

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

Automatic road network extraction in suburban areas from high resolution aerial images

In this paper a road network extraction algorithm for suburban areas is presented. The algorithm uses colour infrared (CIR) images and digital surface models (DSM). The CIR data allow a good separation between vegetation and roads. The image is first segmented in two steps: an initial segmentation using the normalized cuts algorithm and a subsequent grouping of the segments. Road parts are extracted from the segments and then first connected locally to form subgraphs, because roads are often not extracted as a whole due to disturbances in their appearance. Subgraphs can contain several branches, which are resolved by a subsequent optimisation. The optimisation uses criteria describing the relations between the road parts as well as context objects such as trees, vehicles and buildings. The resulting road strings, represented by their centre lines, are then connected to a road network by searching for junctions at the ends of the roads. Small isolated roads are eliminated because they are likely to be false extractions. Results are presented for three image subsets coming from two different data sets, and a quantitative analysis of the completeness and correctness is shown from nine image subsets from the two data sets. The results show that the approach is suitable for the extraction of roads in suburban areas from aerial images

Robust Modular Feature-Based Terrain-Aided Visual Navigation and Mapping

The visual feature-based Terrain-Aided Navigation (TAN) system presented in this thesis addresses the problem of constraining inertial drift introduced into the location estimate of Unmanned Aerial Vehicles (UAVs) in GPS-denied environment. The presented TAN system utilises salient visual features representing semantic or human-interpretable objects (roads, forest and water boundaries) from onboard aerial imagery and associates them to a database of reference features created a-priori, through application of the same feature detection algorithms to satellite imagery. Correlation of the detected features with the reference features via a series of the robust data association steps allows a localisation solution to be achieved with a finite absolute bound precision defined by the certainty of the reference dataset. The feature-based Visual Navigation System (VNS) presented in this thesis was originally developed for a navigation application using simulated multi-year satellite image datasets. The extension of the system application into the mapping domain, in turn, has been based on the real (not simulated) flight data and imagery. In the mapping study the full potential of the system, being a versatile tool for enhancing the accuracy of the information derived from the aerial imagery has been demonstrated. Not only have the visual features, such as road networks, shorelines and water bodies, been used to obtain a position ’fix’, they have also been used in reverse for accurate mapping of vehicles detected on the roads into an inertial space with improved precision. Combined correction of the geo-coding errors and improved aircraft localisation formed a robust solution to the defense mapping application. A system of the proposed design will provide a complete independent navigation solution to an autonomous UAV and additionally give it object tracking capability

An index based road feature extraction from LANDSAT-8 OLI images

Road feature extraction from the remote sensing images is an arduous task and has a significant role in various applications of urban planning, updating the maps, traffic management, etc. In this paper, a new band combination (B652) to form a road index (RI) from OLI multispectral bands based on the spectral reflectance of asphalt, is presented for road feature extraction. The B652 is converted to road index by normalization. The morphological operators (top-hat or bottom-hat) uses on RI to enhance the roads. To sharpen the edges and for better discrimination of features, shock square filter (SSF), is proposed. Then, an iterative adaptive threshold (IAT) based online search with variational min-max and Markov random fields (MRF) model are used on the SSF image to segment the roads and non-roads. The roads are extracting by using the rules based on the connected component analysis. IAT and MRF model segmentation methods prove the proposed index (RI) able to extract road features productively. The proposed methodology is a combination of saturation based adaptive thresholding and morphology (SATM), and saturation based MRF (SMRF), applied to OLI images of several urban cities of India, producing the satisfactory results. The experimental results with the quantitative analysis presented in the paper