Road Extraction Research Papers - Academia.edu (original) (raw)

Automatic network road extraction from high resolution remotely sensed images has been under study by computer scientists for over 30 years. In fact, Conventional methods to create and update road information rely heavily on manual work... more

Automatic network road extraction from high resolution remotely sensed images has been under study by computer scientists for over 30 years. In fact, Conventional methods to create and update road information rely heavily on manual work and therefore are very expensive and time consuming. This paper presents an efficient and computationally fast method to extract road from very high resolution images automatically. We propose in this paper a new approach for following roads path based on a quaternionic wavelet transform insuring a good local space-frequency analysis with very important directional selectivity. In fact, the rich phase information given by this hypercomplex transform overcomes the lack of shift invariance property shown by the real discrete wavelet transform and the poor directional selectivity of both real and complex wavelet transform.

This paper describes a method for extracting roads from a large scale unstructured 3D point cloud of an urban environment consisting of many superimposed scans taken at different times. Given a road map and a point cloud, our system... more

This paper describes a method for extracting roads from a large scale unstructured 3D point cloud of an urban environment consisting of many superimposed scans taken at different times. Given a road map and a point cloud, our system automatically separates road surfaces from the rest of the point cloud. Starting with an approximate map of the road network given in the form of 2D intersection locations connected by polylines, we first produce a 3D representation of the map by optimizing Cardinal splines to minimize the distances to points of the cloud under continuity constraints. We then divide the road network into independent patches, making it feasible to process a large point cloud with a small in-memory working set. For each patch, we fit a 2D active contour to an attractor function with peaks at small vertical discontinuities to predict the locations of curbs. Finally, we output a set of labeled points, where points lying within the active contour are tagged as ``road'' and the others are not. During experiments with a LIDAR point set containing almost a billion points spread over six square kilometers of a city center, our method provides 86% correctness and 94% completeness

The uses of road map in daily activities are numerous but it is a hassle to construct and update a road map whenever there are changes. In Universiti Malaysia Sarawak, research on Automatic Road Extraction (ARE) was explored to solve the... more

The uses of road map in daily activities are numerous
but it is a hassle to construct and update a road map whenever there are changes. In Universiti Malaysia Sarawak, research on Automatic Road Extraction (ARE) was explored to solve the difficulties in updating road map. The research started with using Satellite Image (SI), or in short, the ARE-SI project. A Hybrid Simple Colour Space Segmentation and Edge Detection (Hybrid SCSS-EDGE) algorithm was developed to extract roads automatically from satellite-taken images. In order to extract the road network accurately, the satellite image must be analyzed prior to the extraction process. The characteristics of these elements are analyzed and consequently the relationships among them are determined. In this study, the road regions are extracted based on colour space elements and edge details of roads. Besides, edge detection method is applied to further filter
out the non-road regions. The extracted road regions are validated by using a segmentation method. These results are valuable for building road map and detecting the changes of the existing road database. The proposed Hybrid Simple Colour Space Segmentation and Edge Detection (Hybrid SCSS-EDGE) algorithm can perform the tasks
fully automatic, where the user only needs to input a high-resolution satellite image and wait for the result. Moreover, this system can work on complex road network and generate the extraction result in seconds.

Transportation agencies require up-to-date, reliable, and feasibly acquired information on road geometry and features within proximity to the roads as input for evaluating and prioritizing new or improvement road projects. The information... more

Transportation agencies require up-to-date, reliable, and feasibly acquired information on road geometry and features within proximity to the roads as input for evaluating and prioritizing new or improvement road projects. The information needed for a robust evaluation of road projects includes road centerline, width, and extent together with the average grade, cross-sections, and obstructions near the travelled way. Remote sensing is equipped with a large collection of data and well-established tools for acquiring the information and extracting aforementioned various road features at various levels and scopes. Even with many remote sensing data and methods available for road extraction, transportation operation requires more than the centerlines. Acquiring information that is spatially coherent at the operational level for the entire road system is challenging and needs multiple data sources to be integrated. In the presented study, we established a framework that used data from mu...

Road extraction from digital images is of fundamental importance in the context of automatic mapping, effective urban planning and updating GIS databases. Very high spatial resolution (VHR) imagery acquired by airborne and space borne... more

Road extraction from digital images is of fundamental importance in the context of automatic mapping, effective urban planning and updating GIS databases. Very high spatial resolution (VHR) imagery acquired by airborne and space borne sensors is the main source for accurate road extraction. Manual techniques are fading away as they are time consuming and costly. Hence, road extraction method that is significantly more automated has become a research hotspot in remote sensing information processing. This paper proposes a semi-automatic approach to extract different road types from high-resolution remote sensing images. The approach is based on edge detection and SVM and mathematical morphology method. First the outline of the road is detected based on Canny operator. Then, Full Lambda Schedule merging method combines adjacent segments. Then the entire image was classified using Support Vector Machine (SVM) and various spatial, spectral, and texture attributes to form a road image. Finally, the quality of detected roads is improved using morphological operators. The algorithm was systematically evaluated on a variety of satellite images from Worldview, QuickBird and UltraCam airborne Images. The results of the accuracy evaluation demonstrate that the proposed road extraction approach can provide high accuracy for extraction of different road types.

In this paper an approach to road extraction in open landscape regions from IKONOS multispectral imagery is presented which combines a line-based approach for road extraction with area-based colour segmentation. Existing road databases... more

In this paper an approach to road extraction in open landscape regions from IKONOS multispectral imagery is presented which combines a line-based approach for road extraction with area-based colour segmentation. Existing road databases are used in two ways: firstly, to estimate scene dependent parameters of the line-based approach and secondly to exclude non road regions from the extraction, also exploiting

In this paper we report on research on road extraction at TUM (Technische Universit ̈ at Munchen). We propose a scheme for road extraction in rural areas that integrates three different modules with specific strengths. The first module... more

In this paper we report on research on road extraction at TUM (Technische Universit ̈
at Munchen). We propose a scheme for road extraction in rural areas that integrates three different modules with specific strengths. The first module employs local grouping and uses multiple scales and context to reliably extract most parts of the
road network. In order to connect these parts, the second module exploits the network characteristics of roads for global grouping. The third module completes the network based on an analysis of path lengths within the network. An evaluation of the results shows that the system benefits from the integration of different types of knowledge within the road extraction scheme. In addition to the scheme for rural areas, we present first results of an approach for road extraction in urban areas that focuses on the substructure of roads (markings, lanes) and related objects (vehicles).

Numerous raster maps are available on the Internet, but the geographic coordinates of the maps are often unknown. In order to determine the precise location of a raster map, we exploit the fact that the layout of the road intersections... more

Numerous raster maps are available on the Internet, but the geographic coordinates of the maps are often unknown. In order to determine the precise location of a raster map, we exploit the fact that the layout of the road intersections within a certain area can be used to determine the map's location. In this paper, we describe an approach to automatically extract road intersections from arbitrary raster maps. Identifying the road intersections is difficult because raster maps typically contain multiple layers that ...

Road extraction research has always been an active research on automatic identification of remote sensing images. With the availability of high spatial resolution images from new generation commercial sensors, how to extract roads... more

Road extraction research has always been an active research on automatic identification of remote sensing images. With the availability of high spatial resolution images from new generation commercial sensors, how to extract roads quickly, accurately and automatically has been a cutting-edge problem in remote sensing related fields. In this paper, we present a novel road extraction approach which uses a

we present in this paper an automatic system of urban road extraction from satellite and aerial imagery. Our approach is based on an adaptive directional filtering and a watershed segmentation. The first stage consists of an automatic... more

we present in this paper an automatic system of urban road extraction from satellite and aerial imagery. Our approach is based on an adaptive directional filtering and a watershed segmentation. The first stage consists of an automatic procedure which adapts filtering of each block band to the dominant direction(s) of roads. The choice of the dominant direction(s) is made from a criterion based on the calculation of a factor of direction of detection. The second stage is based on watershed algorithm applied to a Shen-Castan gradient image. This process provides a decision map allowing correcting the errors of the first stage. A ratio of surface on perimeter is used to distinguish among all segments of the image those representing probably roads. Finally, in order to avoid gaps between pieces of roads, the resulting image follows a treatment, based on proximity and colinearity, for linking segments. The proposed approach is tested on common scenes of Landsat ETM+ and aerial imagery of...

A new approach for object extraction from high-resolution satellite images is presented in this paper. The new approach integrates image fusion, multi-spectral classification, feature extraction and feature segmentation into the object... more

A new approach for object extraction from high-resolution satellite images is presented in this paper. The new approach integrates image fusion, multi-spectral classification, feature extraction and feature segmentation into the object extraction of high-resolution satellite images. Both spectral information from multispectral (MS) images and spatial information from panchromatic (Pan) images are utilized for the extraction to improve accuracies. This paper

... 2.1 Morphological Trivial Opening Trivial opening (denoted hereafter TO) is defined by (Serra and Vincent 1992). ... otherwise , criterion the satisfies X(i) if X(i), φ T TO [1] Page 3. Chunsun Zhang, Shunji Murai, Emmanuel P.... more

... 2.1 Morphological Trivial Opening Trivial opening (denoted hereafter TO) is defined by (Serra and Vincent 1992). ... otherwise , criterion the satisfies X(i) if X(i), φ T TO [1] Page 3. Chunsun Zhang, Shunji Murai, Emmanuel P. Baltsavisas 3 ...

Road junctions are important components of a road network. However, they are usually not explicitly modelled in existing road extraction approaches. In this research, we model road junctions in detail as area objects and propose a... more

Road junctions are important components of a road network. However, they are usually not explicitly modelled in existing road extraction approaches. In this research, we model road junctions in detail as area objects and propose a methodology for their automatic extraction through the use of an existing geospatial database. Prior knowledge derived from the geospatial database is used to facilitate

This paper presents a multi-resolution approach for automatic extraction of roads from digital aerial imagery. Roads are modeled as a network of intersections and links between the intersections. For different context regions, i.e.,... more

This paper presents a multi-resolution approach for automatic extraction of roads from digital aerial imagery. Roads are modeled as a network of intersections and links between the intersections. For different context regions, i.e., rural, forest, and urban areas, the model describes relations between background objects, e.g., buildings or trees, and road objects, e.g., road-parts, road-segments, road-links, and intersections. The segmentation of the image into context regions is done by texture analysis. The approach to detect roads is based on the extraction of edges in a high resolution image and the extraction of lines in an image of reduced resolution. Using both resolution levels and explicit knowledge about roads, hypotheses for roadsides are generated. The roadsides are used to construct quadrilaterals representing road-parts and polygons representing intersections.
Neighboring road-parts are chained to road-segments. Road-links, i.e., the roads between two intersections, are built by grouping of road-segments and closing of gaps between road-segments. For the road-segments markings give highly reliable evidence. Road-links are constructed using knowledge about context.

... As their extraction is time consuming, their is a need for automation. ... Approaches relying strongly on this interaction are for instance based on road tracking [15, 23] start ... direction after extracting parallel edges or by... more

... As their extraction is time consuming, their is a need for automation. ... Approaches relying strongly on this interaction are for instance based on road tracking [15, 23] start ... direction after extracting parallel edges or by extrapolation and matching of profiles in high resolution images. ...