Geocoding Services Research Articles

Text Detection and Recognition on Traffic Panels From Street-Level Imagery Using Visual Appearance

Traffic sign detection and recognition has been thoroughly studied for a long time. However, traffic panel detection and recognition still remains a challenge in computer vision due to its different types and the huge variability of the information depicted in them. This paper presents a method to detect traffic panels in street-level images and to recognize the information contained on them, as an application to intelligent transportation systems (ITS). The main purpose can be to make an automatic inventory of the traffic panels located in a road to support road maintenance and to assist drivers. Our proposal extracts local descriptors at some interest keypoints after applying blue and white color segmentation. Then, images are represented as a “bag of visual words” and classified using Naïve Bayes or support vector machines. This visual appearance categorization method is a new approach for traffic panel detection in the state of the art. Finally, our own text detection and recognition method is applied on those images where a traffic panel has been detected, in order to automatically read and save the information depicted in the panels. We propose a language model partly based on a dynamic dictionary for a limited geographical area using a reverse geocoding service. Experimental results on real images from Google Street View prove the efficiency of the proposed method and give way to using street-level images for different applications on ITS.

Accessible and Practical Geocoding Method for Traffic Collision Record Mapping

Numerous studies of geocoding systems have been used to assign geographical coordinates to incident reports identified simply with textual address references. These studies have typically focused on the level of accuracy achieved by various geocoding systems and found that acceptable results can be achieved. Depending on the quality of the input data, a match rate between 70% and 83% can be expected, with varying levels of accuracy. However, few studies have looked at the potential of freely available online geocoding services to spatially locate traffic crash records. It is proposed that although limitations currently exist, services such as the Google Maps API provide sufficient functionality and adequate accuracy for use with a wide variety of geocoding applications. A case study used traffic crash records from a municipality in the province of Quebec, Canada, with the goal of quantifying the geo coding results. It was found that although a competitive match rate was obtained, manual revision was required to ensure that the results returned by the geocoder referred to the same intersection that existed in the input address field.

Accuracy and privacy aspects in free online reverse geocoding services

Reverse geocoding is defined as the extraction of textual information, such as a name or an address, from geographic coordinates. This technique is common in many geo-application scenarios, e.g., in freely available online-based mapping services. However, if personal data are mapped, confidentiality issues may arise, such as if the data are derived from a crime-incidents database. This research provides an evaluation of seven freely available Web mapping services in regard to reverse geocoding accuracy and privacy aspects. The services' textual accuracy is measured using crime data from Vienna, Austria. The accuracy associated with reverse geocoding is derived for districts with different residential densities to determine whether any error variation is associated with the underlying residential structure. Furthermore, this research investigates the reidentification risk when compromising personal information. Using a free online telephone database and addresses in a crime-incidents dataset, the probable names that are linked to crime victims are identified. The results showed that the accuracy ranges from 0% to 23% depending on the predefined precision level. Comparing seven online reverse geocoding services (TravelGIS, Google Maps, nominatim, ArcGIS, Yahoo, Bing, and Geonames), TravelGIS performs better at the highest precision level, whereas Geonames performs better at a lower precision level. However, the results showed no difference among the districts with different residential densities. As for the reidentification risk, 68% probable victims were identified from the database. This work discusses the specific tasks that users have to complete when retrieving addresses associated with crime-incident locations. The article concludes with an evaluation of the reverse geocoding process and a discussion of the privacy risk associated with it.

Geocoding Recommender: An Algorithm to Recommend Optimal Online Geocoding Services for Applications

AbstractToday, many services that can geocode addresses are available to domain scientists and researchers, software developers, and end‐users. For a number of reasons, including quality of reference database and interpolation technique, a given address geocoded by different services does not often result in the same location. Considering that there are many widely available and accessible geocoding services and that each geocoding service may utilize a different reference database and interpolation technique, selecting a suitable geocoding service that meets the requirements of any application or user is a challenging task. This is especially true for online geocoding services which are often used as black boxes and do not provide knowledge about the reference databases and the interpolation techniques they employ. In this article, we present a geocoding recommender algorithm that can recommend optimal online geocoding services by realizing the characteristics (positional accuracy and match rate) of the services and preferences of the user and/or their application. The algorithm is simulated and analyzed using six popular online geocoding services for different address types (agricultural, commercial, industrial, residential) and preferences (match rate, positional accuracy).

Evaluation of the quality of an online geocoding resource in the context of a large Brazilian city

AbstractGeocoding urban addresses usually requires the use of an underlying address database. Under the influence of the format defined for TIGER files decades ago, most address databases and street geocoding algorithms are organized around street centerlines, associating numbering ranges to thoroughfare segments between two street crossings. While this method has been successfully employed in the USA for a long time, its transposition to other countries may lead to increased errors. This article presents an evaluation of the centerline‐geocoding resources provided by Google Maps, as compared to the point‐geocoding method used in the city of Belo Horizonte, Brazil, which we took as a baseline. We generated a textual address for each point object found in the city's point‐based address database, and submitted it to the Google Maps geocoding API. We then compared the resulting coordinates with the ones recorded in Belo Horizonte's GIS. We demonstrate that the centerline segment interpolation method, employed by the online resources following the American practice, has problems that can considerably influence the quality of the geocoding outcome. Completeness and accuracy have been found to be irregular, especially within lower income areas. Such errors in online services can have a significant impact on geocoding efforts related to social applications, such as public health and education, since the online service can be faulty and error‐prone in the most socially demanding areas of the city. In the conclusion, we point out that a volunteered geographic information (VGI) approach can help with the enrichment and enhancement of current geocoding resources, and can possibly lead to their transformation into more reliable point‐based geocoding services.

Comparative evaluation and analysis of online geocoding services

Geocoding is an uncertain process that associates an address or a place name with geographic coordinates. Traditionally, geocoding is performed locally on a stand-alone computer with the geocoding tools usually bundled in GIS software packages. The use of such tools requires skillful operators who know about the issues of geocoding, that is, reference databases and complicated geocoding interpolation techniques. These days, with the advancement in the Internet and Web services technologies, online geocoding provides its functionality to the Internet users with ease; thus, they are often unaware of such issues. With an increasing number of online geocoding services, which differ in their reference databases, the geocoding algorithms, and the strategy for dealing with inputs and outputs, it is crucial for the service requestors to realize the quality of the geocoded results of each service before choosing one for their applications. This is primarily because any errors associated with the geocoded addresses will be propagated to subsequent decisions, activities, modeling, and analysis. This article examines the quality of five online geocoding services: Geocoder.us, Google, MapPoint, MapQuest, and Yahoo!. The quality of each geocoding service is evaluated with three metrics: match rate, positional accuracy, and similarity. A set of addresses from the US Environmental Protection Agency (EPA) database were used as a baseline. The results were statistically analyzed with respect to different location characteristics. The outcome of this study reveals the differences among the online geocoding services on the quality of their geocoding results and it can be used as a general guideline for selecting a suitable service that matches an application's needs.

Quality Assessment of Online Street and Rooftop Geocoding Services

The widespread use of Internet-based mapping and geospatial analysis has caused an increase in the demand for online geocoding services. Although such services provide convenience, low (or free) cost and immediate solutions, their characteristics, sometimes, overshadow the expectation of producing quality of geocoded results. In recent years, several geocoding techniques have emerged, including rooftop geocoding, but they have yet to receive much attention in the literature. This paper examines and compares the quality of online rooftop and street geocoding services based on match rates and positional accuracy. Six geocoding services by five providers (i.e., Microsoft Virtual Earth, Google, Geocoder.us, MapQuest, and Yahoo!) were evaluated using addresses in Allegheny County, Pennsylvania. Results of the comparison indicate that rooftop geocoding produces slightly lower match rates but significantly higher positional accuracy than street geocoding. The hybrid service, which combines the two techniques, produces match rates as high as other street geocoding services but improves in positional accuracy close to the level of rooftop geocoding. Geocoding services employing reference databases with similar quality trend to produce compatible match rates and positional accuracy. This paper examines the sensitivity of different address types on geocoding quality. The results reveal that both rooftop and street geocoding produce high match rates and high accuracy for residential addresses. However, positional accuracies of agricultural and industrial address types are not very reliable due to the small sample sizes. With these, it is recommended to use online rooftop geocoding services if high positional accuracy is the priority, use street geocoding if high match rate is the priority, and use the hybrid approach if both high match rates and high positional accuracy are required.

Geocoding Quality and Implications for Spatial Analysis

AbstractMany spatial analysis techniques rely on the ability to geocode individual locations based on addresses or other descriptive information. The quality of geocoding and its effect on spatial analysis have received some attention in the literature, in particular in the field of health. This article reviews the foundation of geocoding and presents a framework for evaluating geocoding quality. Errors introduced by street gecoding include incompleteness, positional error, and incorrect assignment to geographic units. A review of empirical studies suggests that these errors are neither small nor random in nature and that substantial bias may be introduced in spatial analysis that employs the results of geocoding. Several alternatives have also emerged, including the use of address points and parcels, and these are gradually becoming more widely used. Several areas for future research on geocoding have been identified: (i) refinements of address data models to incorporate complex addressing situations; (ii) development of error propagation techniques to determine the level of geocoding quality required for a particular analysis scenario; (iii) development of measures of reliability for geocoding results; (iv) comparative analysis of geocoding quality across different jurisdictions; and (v) validation of online geocoding services and volunteered geographic information.