Improving the Quality of Citizen Contributed Geodata through Their Historical Contributions:The Case of the Road Network in OpenStreetMap

OpenStreetMap (OSM) has proven to serve as a promising free global encyclopedia of maps with an increasing popularity across different user communities and research bodies. One of the unique characteristics of OSM has been the availability of the full history of users’ contributions, which can leverage our quality control mechanisms through exploiting the history of contributions. Since this aspect of contributions (i.e., historical contributions) has been neglected in the literature, this study aims at presenting a novel approach for improving the positional accuracy and completeness of the OSM road network. To do so, we present a five-stage approach based on a Voronoi diagram that leads to improving the positional accuracy and completeness of the OSM road network. In the first stage, the OSM data history file is retrieved and in the second stage, the corresponding data elements for each object in the historical versions are identified. In the third stage, data cleaning on the historical datasets is carried out in order to identify outliers and remove them accordingly. In the fourth stage, through applying the Voronoi diagram method, one representative version for each set of historical versions is extracted. In the final stage, through examining the spatial relations for each object in the history file, the topology of the target object is enhanced. As per validation, a comparison between the latest version of the OSM data and the result of our approach against a reference dataset is carried out. Given a case study in Tehran, our findings reveal that the completeness and positional precision of OSM features can be improved up to 14%. Our conclusions draw attention to the exploitation of the historical archive of the contributions in OSM as an intrinsic quality indicator

Analyzing the Tagging Quality of the Spanish OpenStreetMap

In this paper, a framework for the assessment of the quality of OpenStreetMap is presented, comprising a batch of methods to analyze the quality of entity tagging. The approach uses Taginfo as a reference base and analyses quality measures such as completeness, compliance, consistence, granularity, richness and trust . The framework has been used to analyze the quality of OpenStreetMap in Spain, comparing the main cities of Spain. Also a comparison between Spain and some major European cities has been carried out. Additionally, a Web tool has been also developed in order to facilitate the same kind of analysis in any area of the world

Towards an Automated Comparison of OpenStreetMap with Authoritative Road Datasets

OpenStreetMap (OSM) is an extraordinarily large and diverse spatial database of the world. Road networks are amongst the most frequently occurring spatial content within the OSM database. These road network representations are usable in many applications. However the quality of these representations can vary between locations. Comparing OSM road networks with authoritative road datasets for a given area or region is an important task in assessing OSM’s fitness for use for applications like routing and navigation. Such comparisons can be technically challenging and no software implementation exists which facilitates them easily and automatically. In this article we develop and propose a flexible methodology for comparing the geometry of OSM road network data with other road datasets. Quantitative measures for the completeness and spatial accuracy of OSM are computed, including the compatibility of OSM road data with other map databases. Our methodology provides users with significant flexibility in how they can adjust the parameterization to suit their needs. This software implementation is exclusively built on open source software and a significant degree of automation is provided for these comparisons. This software can subsequently be extended and adapted for comparison between OSM and other external road datasets

Fusion of Heterogeneous Earth Observation Data for the Classification of Local Climate Zones

This paper proposes a novel framework for fusing multi-temporal, multispectral satellite images and OpenStreetMap (OSM) data for the classification of local climate zones (LCZs). Feature stacking is the most commonly-used method of data fusion but does not consider the heterogeneity of multimodal optical images and OSM data, which becomes its main drawback. The proposed framework processes two data sources separately and then combines them at the model level through two fusion models (the landuse fusion model and building fusion model), which aim to fuse optical images with landuse and buildings layers of OSM data, respectively. In addition, a new approach to detecting building incompleteness of OSM data is proposed. The proposed framework was trained and tested using data from the 2017 IEEE GRSS Data Fusion Contest, and further validated on one additional test set containing test samples which are manually labeled in Munich and New York. Experimental results have indicated that compared to the feature stacking-based baseline framework the proposed framework is effective in fusing optical images with OSM data for the classification of LCZs with high generalization capability on a large scale. The classification accuracy of the proposed framework outperforms the baseline framework by more than 6% and 2%, while testing on the test set of 2017 IEEE GRSS Data Fusion Contest and the additional test set, respectively. In addition, the proposed framework is less sensitive to spectral diversities of optical satellite images and thus achieves more stable classification performance than state-of-the art frameworks.Comment: accepted by TGR

An automated GRASS-based procedure to assess the geometrical accuracy of the OpenStreetMap Paris road network

OpenStreetMap (OSM) is the largest spatial database of the world. One of the most frequently occurring geospatial elements within this database is the road network, whose quality is crucial for applications such as routing and navigation. Several methods have been proposed for the assessment of OSM road network quality, however they are often tightly coupled to the characteristics of the authoritative dataset involved in the comparison. This makes it hard to replicate and extend these methods. This study relies on an automated procedure which was recently developed for comparing OSM with any road network dataset. It is based on three Python modules for the open source GRASS GIS software and provides measures of OSM road network spatial accuracy and completeness. Provided that the user is familiar with the authoritative dataset used, he can adjust the values of the parameters involved thanks to the flexibility of the procedure. The method is applied to assess the quality of the Paris OSM road network dataset through a comparison against the French official dataset provided by the French National Institute of Geographic and Forest Information (IGN). The results show that the Paris OSM road network has both a high completeness and spatial accuracy. It has a greater length than the IGN road network, and is found to be suitable for applications requiring spatial accuracies up to 5-6 m. Also, the results confirm the flexibility of the procedure for supporting users in carrying out their own comparisons between OSM and reference road datasets

Quality Assessment of the Canadian OpenStreetMap Road Networks

Volunteered geographic information (VGI) has been applied in many fields such as participatory planning, humanitarian relief and crisis management because of its cost-effectiveness. However, coverage and accuracy of VGI cannot be guaranteed. OpenStreetMap (OSM) is a popular VGI platform that allows users to create or edit maps using GPS-enabled devices or aerial imageries. The issue of geospatial data quality in OSM has become a trending research topic because of the large size of the dataset and the multiple channels of data access. The objective of this study is to examine the overall reliability of the Canadian OSM data. A systematic review is first presented to provide details on the quality evaluation process of OSM. A case study of London, Ontario is followed as an experimental analysis of completeness, positional accuracy and attribute accuracy of the OSM street networks. Next, a national study of the Canadian OSM data assesses the overall semantic accuracy and lineage in addition to the quality measures mentioned above. Results of the quality evaluation are compared with associated OSM provenance metadata to examine potential correlations. The Canadian OSM road networks were found to have comparable accuracy with the tested commercial database (DMTI). Although statistical analysis suggests that there are no significant relations between OSM accuracy and its editing history, the study presents the complex processes behind OSM contributions possibly influenced by data import and remote mapping. The findings of this thesis can potentially guide cartographic product selection for interested parties and offer a better understanding of future quality improvement in OSM

Positional accuracy assessment of the OpenStreetMap buildings layer through automatic homologous pairs detection: the method and a case study

OpenStreetMap (OSM) is currently the largest openly licensed collection of geospatial data. Being OSM increasingly exploited in a variety of applications, research has placed great attention on the assessment of its quality. This work focuses on assessing the quality of OSM buildings. While most of the studies available in literature are limited to the evaluation of OSM building completeness, this work proposes an original approach to assess the positional accuracy of OSM buildings based on comparison with a reference dataset. The comparison relies on a quasi-automated detection of homologous pairs on the two datasets. Based on the homologous pairs found, warping algorithms like e.g. affine transformations and multi-resolution splines can be applied to the OSM buildings to generate a new version having an optimal local match to the reference layer. A quality assessment of the OSM buildings of Milan Municipality (Northern Italy), having an area of about 180 km2, is then presented. After computing some measures of completeness, the algorithm based on homologous points is run using the building layer of the official vector cartography of Milan Municipality as the reference dataset. Approximately 100000 homologous points are found, which show a systematic translation of about 0.4 m on both the X and Y directions and a mean distance of about 0.8 m between the datasets. Besides its efficiency and high degree of automation, the algorithm generates a warped version of OSM buildings which, having by definition a closest match to the reference buildings, can be eventually integrated in the OSM database

AUTHORITATIVE CARTOGRAPHY IN BRAZIL AND COLLABORATIVE MAPPING PLATFORMS: CHALLENGES AND PROPOSALS FOR DATA INTEGRATION

Brazil has a large area with missing or outdated mapping on the largest scales of its authoritative mapping. The use of data from collaborative mapping platforms appears as an alternative that may contribute to minimizing this problem, either by updating or completing the mapping coverage in Brazil, as proposed or performed by some National Mapping Agencies abroad. The present work aims to analyze a methodology to provide accurate and documented integration of volunteered geographic information and the Brazilian authoritative mapping. The proposal starts with the semantic compatibility between the conceptual models adopted in both official cartography and OpenStreetMap platform. The research continues with the identification of object classes with the most significant potential for integration. Finally, we developed some experiments to evaluate and validate the OSM data integration process in a 1:25,000 scale cartographic database. Even in regions with a recent mapping, the results of the preliminary assessment indicate the potential for an increase of about 52% and 16% of features in the ‘road system’ category, which suggests a very promising method for use in areas with missing or outdated mapping, and its applicability to other categories

Open source data mining infrastructure for exploring and analysing OpenStreetMap

OpenStreetMap and other Volunteered Geographic Information datasets have been explored in the last years, with the aim of understanding how their meaning is rendered, of assessing their quality, and of understanding the community-driven process that creates and maintains the data. Research mostly focuses either on the data themselves while ignoring the social processes behind, or solely discusses the community-driven process without making sense of the data at a larger scale. A holistic understanding that takes these and other aspects into account is, however, seldom gained. This article describes a server infrastructure to collect and process data about different aspects of OpenStreetMap. The resulting data are offered publicly in a common container format, which fosters the simultaneous examination of different aspects with the aim of gaining a more holistic view and facilitates the results’ reproducibility. As an example of such uses, we discuss the project OSMvis. This project offers a number of visualizations, which use the datasets produced by the server infrastructure to explore and visually analyse different aspects of OpenStreetMap. While the server infrastructure can serve as a blueprint for similar endeavours, the created datasets are of interest themselves too