Road Vector Research Articles

CONFLATION OF NATIONAL BRIDGE INVENTORY DATABASE WITH TIGERBASED ROAD VECTORS

Abstract. The National Bridge Inventory (NBI) database provides detailed bridge information with full coverage in the United States. In the database, each bridge record has an associated geographic location, which makes it possible to match the NBI records with road vectors in a geospatial database. This paper presents our approach to conflating the NBI database with U.S. Census Bureau's Topologically Integrated Geographic Encoding and Referencing (TIGER) road vectors based on a point-to-line matching algorithm, which combines both geometric and semantic similarity measurements. Experiments have shown that most NBI bridges can be successfully matched to TIGER road vectors with an overall success rate over 80%. The matched results are then used to help define linear features for bridges in the road database. The resultant road databases have enhanced bridge information and are further used in creating a 3D road database. The proposed methodology has been fully implemented and has been used in various applications.

Geospatial Information Integration for Authoritative and Crowd Sourced Road Vector Data

AbstractThis article describes results from a research project undertaken to explore the technical issues associated with integrating unstructured crowd sourced data with authoritative national mapping data. The ultimate objective is to develop methodologies to ensure the feature enrichment of authoritative data, using crowd sourced data. Users increasingly find that they wish to use data from both kinds of geographic data sources. Different techniques and methodologies can be developed to solve this problem. In our previous research, a position map matching algorithm was developed for integrating authoritative and crowd sourced road vector data, and showed promising results (Anand et al. 2010). However, especially when integrating different forms of data at the feature level, these techniques are often time consuming and are more computationally intensive than other techniques available. To tackle these problems, this project aims at developing a methodology for automated conflict resolution, linking and merging of geographical information from disparate authoritative and crowd‐sourced data sources. This article describes research undertaken by the authors on the design, implementation, and evaluation of algorithms and procedures for producing a coherent ontology from disparate geospatial data sources. To integrate road vector data from disparate sources, the method presented in this article first converts input data sets to ontologies, and then merges these ontologies into a new ontology. This new ontology is then checked and modified to ensure that it is consistent. The developed methodology can deal with topological and geometry inconsistency and provide more flexibility for geospatial information merging.

Tracking of Vector Roads for the Determination of Seams in Aerial Image Mosaics

This letter proposes a novel approach using vector roads to aid in generating seams for aerial image mosaicking. A representative seam of two adjacent images is extracted as follows. First, the vector roads in the overlapping area of adjacent images are overlaid with the straight skeleton of the overlapping area to build a weighted graph G(V, E). Dijkstra's algorithm is then applied to And the lowest cost path in G(V, E) that connects two intersections of the boundary polygons of adjacent images. The lowest cost path is considered as a seam candidate. Second, the seam candidate is refined by considering its surrounding pixels. The refined seam is employed as the final seam. Experiments demonstrate that this vector-based approach is typically more efficient than the existing raster-based approaches, particularly when vector road networks are available.

A general approach for extracting road vector data from raster maps

Raster maps are easily accessible and contain rich road information; however, converting the road information to vector format is challenging because of varying image quality, overlapping features, and typical lack of metadata (e.g., map geocoordinates). Previous road vectorization approaches for raster maps typically handle a specific map series and require significant user effort. In this paper, we present a general road vectorization approach that exploits common geometric properties of roads in maps for processing heterogeneous raster maps while requiring minimal user intervention. In our experiments, we compared our approach to a widely used commercial product using 40 raster maps from 11 sources. We showed that overall our approach generated high-quality results with low redundancy with considerably less user input compared with competing approaches.

항공사진과 도로 벡터 간의 Alignment를 위한 Vector Median Filter의 적용

대용량의 공간정보의 인터넷을 통한 활용이 활성화되면서 더 정확하고 풍부하며 최신의 정보를 가진 자료에 대한 사용자의 요구는 꾸준히 증가하고 있어 기 구축된 자료 간 융합을 통합 새로운 서비스 제공의 필요성이 커지고 있다. 이중에서 항공사진과 도로 벡터 자료를 융합한 서비스는 직관적이고 정확하게 정보를 전달 할 수 있어 많은 포털사이트에서 제공중인 서비스이나 자료 간 공간 불일치를 해결해야 한다. 본 연구에서는 도로 후보 영상 추출, 템플릿 매칭, 벡터 미디언 필터를 거쳐 자료 간 조정을 수행하였으며 완성도와 부합도로 정확도를 평가한 결과 원래 데이터에 비해 약 35% 향상되었음을 확인할 수 있었다. Recent growth of the geospatial information on the web made it possible to applicate spatial data. Also, the demand for rich and latest information shows a steady growth. The need for the new service using conflation of the existing spatial databases is on the increase. The information delivery of the services using the road vector and aerial image is reached intuitionally and accurately. However, the spatial inconsistencies in map services such as Daum map, Naver map and Google map is the problem. Our approach is processed to extract the road candidate image, match the template and filter the control points pair using vector median. Finally, CNS node and link are aligned to the real road with the aerial image. The experimental results show that our approach can align a set of CNS node and link with aerial imagery for daejon, such that the completeness and correctness of the aligned road have improved about 35% compare with the original roads.

Relaxation‐Based Point Feature Matching for Vector Map Conflation

AbstractWith the rapid advance of geospatial technologies, the availability of geospatial data from a wide variety of sources has increased dramatically. It is beneficial to integrate / conflate these multi‐source geospatial datasets, since the integration of multi‐source geospatial data can provide insights and capabilities not possible with individual datasets. However, multi‐source datasets over the same geographical area are often disparate. Accurately integrating geospatial data from different sources is a challenging task. Among the subtasks of integration/conflation, the most crucial one is feature matching, which identifies the features from different datasets as presentations of the same real‐world geographic entity. In this article we present a new relaxation‐based point feature matching approach to match the road intersections from two GIS vector road datasets. The relaxation labeling algorithm utilizes iterated local context updates to achieve a globally consistent result. The contextual constraints (relative distances between points) are incorporated into the compatibility function employed in each iteration's updates. The point‐to‐point matching confidence matrix is initialized using the road connectivity information at each point. Both the traditional proximity‐based approach and our relaxation‐based point matching approach are implemented and experiments are conducted over 18 test sites in rural and suburban areas of Columbia, MO. The test results show that our relaxation labeling approach has much better performance than the proximity matching approach in both simple and complex situations.

Road Extraction from Satellite Images using a Fuzzy-Snake Model

This paper proposes a developed approach to extract roads from optical remotely sensed images. The approach is based on the following steps. First, a window with size of 5 × 5 pixels is moved over the image to calculate the features: mean (x1), standard deviation (x2), skewness (x3) and kurtosis (x4). Then, the roads are identified based on the converted features to the specific fuzzy sets of the linguistic variables. The used linguistic variables are Mean, Standard deviation, Skewness, Kurtosis and Grey-scale with trapezoid and triangle membership functions. Next, the skeleton of the identified roads is extracted using two structure elements from the mathematical morphology. Finally, a snake model is employed to extract the road vector form from the skeletons. The results of the accuracy evaluation demonstrate that the developed road extraction approach can provide both good visual and high positional accuracy. The approach is tested over the samples of SPOT-4 panchromatic images from areas in Iran.

Improving Estimates of Simulated Runoff Quality and Quantity Using Road-Enhanced Land Cover Data

Hydrologic model estimates of runoff quantity and quality are appropriately sensitive to the extent and distribution of impervious area (IA) inputs. This research enhances land use maps of IA extracted from road networks and demonstrates its impact on hydrologic model parameters for 704 watersheds in New York. National Land Cover Database (NLCD) 1992 and 2001 data at 30 m pixel resolution provided baseline maps, and enhancement involved reclassifying pixels with overlapping high accuracy vector roads. NLCD 1992 is limited by its lack of explicit IA estimates, instead relying on wide ranges of IA for developed classes, and by the failure of its transportation class to capture most roads. NLCD 2001 has no transportation class, and its explicit IA estimates were found to underestimate IA. Road enhancement caused significant increases in hydrologic parameters such as the curve number, runoff coefficient, and event mean concentration based pollutant loads, with a p<1E-3 for a paired, two-tailed t -test. Magnit...

Automated Geospatial Conflation of Vector Road Maps to High Resolution Imagery

As the availability of various geospatial data increases, there is an urgent need to integrate multiple datasets to improve spatial analysis. However, since these datasets often originate from different sources and vary in spatial accuracy, they often do not match well to each other. In addition, the spatial discrepancy is often nonsystematic such that a simple global transformation will not solve the problem. Manual correction is labor-intensive and time-consuming and often not practical. In this paper, we present an innovative solution for a vector-to-imagery conflation problem by integrating several vector-based and image-based algorithms. We only extract the different types of road intersections and terminations from imagery based on spatial contextual measures. We eliminate the process of line segment detection which is often troublesome. The vector road intersections are matched to these detected points by a relaxation labeling algorithm. The matched point pairs are then used as control points to perform a piecewise rubber-sheeting transformation. With the end points of each road segment in correct positions, a modified snake algorithm maneuvers intermediate vector road vertices toward a candidate road image. Finally a refinement algorithm moves the points to center each road and obtain better cartographic quality. To test the efficacy of the automated conflation algorithm, we used U.S. Census Bureau's TIGER vector road data and U.S. Department of Agriculture's 1-m multi-spectral near infrared aerial photography in our study. Experiments were conducted over a variety of rural, suburban, and urban environments. The results demonstrated excellent performance. The average correctness measure increased from 20.6% to 95.5% and the average root-mean-square error decreased from 51.2 to 3.4 m.

Image‐based quality assessment of road databasesX

In this paper an approach to the automatic quality assessment of existing geo‐spatial data is presented. The necessary reference information is derived automatically from up‐to‐date digital remotely sensed images using image analysis methods. The focus is on the quality assessment of roads as these are among the most frequently changing objects in the landscape. In contrast to existing approaches for quality control of road data, the data to be assessed and the objects extracted from the images are modelled and processed together. A geometric‐topologic relationship model for the roads and their surroundings is defined. Context objects such as rows of trees support the quality assessment of road vector data as they may explain gaps in road extraction. The extraction and explicit incorporation of these objects in the assessment of a given road database give stronger support for or against its correctness. X. Significantly enhanced and extended version of Gerke and Heipke (2006) During the assessment existing relations between road objects from the database and extracted objects are compared to the modelled relations. The certainty measures of the objects are integrated into this comparison. Normally, more than one extracted object gives evidence for a road database object; therefore, a reasoning algorithm which combines evidence given by the extracted objects is used. If the majority of the total evidence argues for the database object and if a certain amount of this database object is covered by extracted objects, the database object is assumed to be correct, i.e. it is accepted, otherwise it is rejected. The procedure is embedded into a two‐stage graph‐based approach which exploits the connectivity of roads and results in a reduction of false alarms. The algorithms may be incorporated into a semi‐automatic environment, where a human operator only checks those objects that have been rejected. The experimental results confirm the importance of the employed advanced statistical modelling. The overall approach can reliably assess the roads from the given database, using road and context objects which have been automatically extracted from remotely sensed imagery. Sensitivity analysis shows that in most cases the chosen two‐stage graph‐approach reduces the number of false decisions. Approximately 66% of the road objects have been accepted by the developed approach in an extended test area, 1% has been accepted though incorrect. Those false decisions are mainly related to the lack of modelling road junction areas.

Extracting Urban Road Networks from High-resolution True Orthoimage and Lidar

Automated or semi-automated feature extraction from remotely collected, large scale image data has been a challenging issue in digital photogrammetry for many years. In the feature extraction field, fusing different types of data to provide complementary information about the objects is becoming increasingly important. In this paper, we present a newly developed approach for the automatic extraction of urban area road networks from a true orthoimage and lidar assuming the road network to be a semi-grid pattern. The proposed approach starts from the subdivision of a study area into small regions based on homogeneity of the dominant road directions from the true orthoimage. Each region’s road candidates are selected with a proposed free passage measure. This process is called the “acupuncture” method. Features around the road candidates are used as key factors for an advanced “acupuncture method” called the region-based acupuncture method. Extracted road candidates are edited to avoid collocation with non-road features such as buildings and grass fields. In order to produce a building map for the prior step, a first-last return analysis and morphological filter are used with the lidar point cloud. A grass area thematic map is generated by supervised classification techniques from a synthetic image, which contains the three color bands from the true orthoimage and the lidar intensity value. Those non-road feature maps are used as a blocking mask for the roads. The accuracy of the result is evaluated quantitatively with respect to manually compiled road vectors, and a completeness of 80 percent and a correctness of 79 percent are obtained with the proposed algorithm on an area of 1,081,600 square meters.

Detection and Vectorization of Roads from Lidar Data

A method for the automatic detection and vectorization of roads from lidar data is presented. To extract roads from a lidar point cloud, a hierarchical classification technique is used to classify the lidar points progressively into road and non-road points. During the classification process, both intensity and height values are initially used. Due to the homogeneous and consistent nature of roads, a local point density is introduced to finalize the classification. The resultant binary classification is then vectorized by convolving a complex-valued disk named the Phase Coded Disk (PCD) with the image to provide three separate pieces of information about the road. The centerline and width of the road are obtained from the resultant magnitude image while the direction is determined from the corresponding phase image, thus completing the vectorized road model. All algorithms used are described and applied to two urban test sites. Completeness values of 0.88 and 0.79 and correctness values of 0.67 and 0.80 were achieved for the classification phase of the process. The vectorization of the classified results yielded RMS values of 1.56 m and 1.66 m, completeness values of 0.84 and 0.81 and correctness values of 0.75 and 0.80 for two different data sets.

Vehicle detection from high-resolution satellite imagery using morphological shared-weight neural networks

High-resolution satellite imagery has recently become a new data source for extraction of small-scale objects such as vehicles. Very little vehicle detection research has been done using high-resolution satellite imagery where panchromatic band resolutions are presently in the range of 0.6–1.0 m. Given the limited spatial resolution, reliable vehicle detection can only be achieved by incorporating contextual information. Here, a GIS road vector map is used to constrain a vehicle detection system to road networks. We used a morphological shared-weight neural network (MSNN) to learn an implicit vehicle model and classify pixels into vehicles and non-vehicles. A vehicle image base library was built by collecting more than 300 cars manually from test images. Strategies to reduce the false alarms and select target centroids were designed. Experimental results indicate that the MSNN performed very well. The detection rate on both training and validation sites exceeded 85% with very few false alarms. By learning the implicit vehicle model through a MSNN, our method outperforms a baseline blob detection method.

Automatically Conflating Road Vector Data with Orthoimagery

Recent growth of the geospatial information on the web has made it possible to easily access a wide variety of spatial data. The ability to combine various sets of geospatial data into a single composite dataset has been one of central issues of modern geographic information processing. By conflating diverse spatial datasets, one can support a rich set of queries that could have not been answered given any of these sets in isolation. However, automatically conflating geospatial data from different data sources remains a challenging task. This is because geospatial data obtained from various data sources may have different projections, different accuracy levels and different formats (e.g., raster or vector format), thus resulting in various positional inconsistencies. Most of the existing algorithms only deal with vector to vector data conflation or require human intervention to accomplish vector data to imagery conflation. In this paper, we describe a novel geospatial data fusion approach, named AMS-Conflation, which achieves automatic vector to imagery conflation. We describe an efficient technique to automatically generate control point pairs from the orthoimagery and vector data by exploiting the information from the vector data to perform localized image processing on the orthoimagery. We also evaluate a filtering technique to automatically eliminate inaccurate pairs from the generated control points. We show that these conflation techniques can automatically align the roads in orthoimagery, such that 75% of the conflated roads are within 3.6 meters from the real road axes compared to 35% for the original vector data for partial areas of the county of St. Louis, MO.

Automatic vectorization of segmented road networks by geometrical and topological analysis of high resolution binary images

In this paper, a new method in order to achieve the geometrical and topological definition of extracted road networks is presented. Starting from a raster binary image where a road network is depicted, this algorithm seeks the automatic raster – vector conversion based on skeleton extraction and graph theory and using GIS database if it is available. The last goal of this method is to provide a numerical structured file which includes the geometric definition for all roads as well as the topologic relations between them. The applied technique comprises six steps. In the first step, the quality of the binary image is improved through a noise cleaning process. In the second step, parallel edges of road network are smoothed by means a generalization process. In the third step, skeleton is extracted applying a known and efficient method which some years ago was published. The fourth step consists in constructing the graph and generating the different cartographic objects which compose the road network. In this phase, GIS information can be used in order to improve the result. In the fifth step, objects are numerically adjusted by means of polynomial adjustment in the opened objects case, and using a reiterative polygonal adjustment in the sharp objects case. In the last step, mathematical morphology is applied to validate topologically the geometrical adjustment. For it, the junction nodes are analyzed for changing automatically their coordinates in order to achieve a topologically correct road network vectorization. Finally, objects are structured according to cartographic criteria and a numerical file with the vectorized road network is provided. Experimental results show the validity of this approach.

Graph‐based change detection in geographic information using VHR satellite images

In this paper we examine a system based on computer vision for automated detection of change and anomalies in GIS road networks using very high resolution satellite images. The system consists of a low‐level feature detection process, which extracts road junctions, and a high‐level matching process, which uses graph matching to find correspondences between the detected image information and the road vector data. The matching process is based on continuous relaxation labelling. It is driven by spatial relations between the objects and takes into account different errors that can occur. The result is an object‐to‐object mapping between image and vector dataset. The mapping result can be used to calculate a rubbersheeting transformation which is able to compensate for local distortions. A measure of change is defined based on the number of null assignments. We show how combined with a condition to characterize acceptable errors, this measure is useful and reliable to characterize inconsistencies between image and vector data.

A Snake-based Approach for TIGER Road Data Conflation

The TIGER (Topologically Integrated Geographic Encoding and Referencing) system has served the U.S. Census Bureau and other agencies' geographic needs successfully for two decades. Poor positional accuracy has however made it extremely difficult to integrate TIGER with advanced technologies and data sources such as GPS, high resolution imagery, and state/local GIS data. In this paper, a potential solution for conflation of TIGER road centerline data with other geospatial data is presented. The first two steps of the approach (feature matching and map alignment) remain the same as in traditional conflation. Following these steps, a third is added in which active contour models (snakes) are used to automatically move the vertices of TIGER roads to high-accuracy roads, rather than transferring attributes between the two datasets. This approach has benefits over traditional conflation methodology. It overcomes the problem of splitting vector road line segments, and it can be extended for vector imagery conflation as well. Thus, a variety of data sources (GIS, GPS, and Remote Sensing) could be used to improve TIGER data. Preliminary test results indicate that the three-step approach proposed in this paper performs very well. The positional accuracy of TIGER road centerline can be improved from an original 100 plus meters' RMS error to only 3 meters. Such an improvement can make TIGER data more useful for much broader application.

Automated Road Extraction from High Resolution Multispectral Imagery

This work presents a novel methodology for fully automated road centerline extraction that exploits spectral content from high resolution multispectral images. Preliminary detection of candidate road centerline components is performed with Anti-parallel-edge Centerline Extraction (ACE). This is followed by constructing a road vector topology with a fuzzy grouping model that links nodes from a self-organized mapping of the ACE components. Following topology construction, a Self-Supervised Road Classification (SSRC) feedback loop is implemented to automate the process of training sample selection and refinement for a road class, as well as deriving practical spectral definitions for non-road classes. SSRC demonstrates a potential to provide dramatic improvement in road extraction results by exploiting spectral content. Road centerline extraction results are presented for three 1 m colorinfrared suburban scenes which show significant improvement following SSRC.

Road vectors update using SAR imagery: a snake-based method

The paper presents an approach for roads detection based on synthetic aperture radar (SAR) images and road databases. The vectors provided by the database are refined using active contours (snakes). In this framework, we firstly develop a restoration filter based on the frost filter achieving an acceptable compromise between speckle elimination and lines preserving. This is followed by a line plausibility calculation step which is used to deform the snake from its initial location toward the final solution. The snake is reformulated using finite elements method. The setting of the snake parameters is not an obvious problem especially when they are tuned by trial-and-error process. We propose a new automatic computational rule for the snake parameters. Our approach is validated by a series of tests on synthetic and SAR images.

Road network simplification with knowledge-based spatial analysis

A typical requirement in digital society is the rapid distribution and effective application of digital products. Geographical data bases are far more complex than other ones in that both attribute and spatial data have to be stored and handled under the same framework. On the other hand, the number of end users of digital cartographic data is explosively increasing, ranging from professional map makers to vehicle drivers and individual tourists. Thus arises a question: how can a data supplier give the “best” service to each user? In nature it involves such difficult problems as cartographic generalization and map simplification. In this paper, the author argues that the visual knowledge or perception experience can be effectively exploited and used to guide the process of data simplification. We have developed a prototype system with a subset of the vector road database covering Munich city.