Quality Of OpenStreetMap Data Research Articles

Research on multiscale OpenStreetMap in China: data quality assessment with EWM-TOPSIS and GDP modeling

ABSTRACT OpenStreetMap (OSM) is a voluntary platform designed to provide free and up-to-date geographic data. Since OSM is based on multisource geographic data provided by the public, the quality of the data has become a concern of researchers. In this study, a unified measure for evaluating OSM data quality was constructed, and the entropy weight method (EWM) and Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) model were used to evaluate the quality of multiscale OSM data in China from 2014 to 2020. In addition to evaluating the data quality, the use of OSM data quality index factors in economic modeling at different spatial scales in China was explored by using a geographic information system (GIS) analysis method and a geographically and temporally weighted regression (GTWR) model. Four machine learning models, SVM, RF, XGBoost and CatBoost, were used to simulate the grid-scale GDP, and the effectiveness of these simulations was discussed. The results showed that (1) the weights of OSM data quality indicator factors vary across different spatial scales. (2) From 2014 to 2020, the quality of national-scale OSM data first increased, then decreased and then gradually stabilized. In addition, the quality of OSM data at the provincial and municipal scales is significantly different, and the distribution is affected by the population and geographical environment. (3) Over time, the spatial clustering characteristics of OSM data quality at different spatial scales in China has continuously strengthened. In addition, the quality of Chinese OSM data displays obvious local spatial autocorrelation characteristics, which are dominated by H-H clustering and L-L clustering. (4) The GTWR model performs well in predicting and revealing the spatiotemporal correlation characteristics between GDP and OSM data quality indicators at the provincial and municipal scales in China. The correlations increase with decreasing spatial scale (provincial to municipal). Moreover, the GDP modeling ability is better in economically underdeveloped Northwest China and economically developed East China. (5) Four machine learning models coupled with road network length completeness, relative linear density, road name attribute completeness, POI name attribute completeness, road network accuracy, road network update frequency, POI update frequency, topological consistency and directional similarity yielded the best grid-scale GDP simulation values. Notably, the CatBoost model provided the best accuracy, and the results further verified that it is feasible to use the proposed OSM data quality index system to predict the regional economic development level in China.

Exploring the accuracy and completeness patterns of global land-cover/land-use data in OpenStreetMap

OpenStreetMap (OSM) can supply useful information to improve land-cover/land-use (LCLU) mapping. However, many concerns have been paid attention to OSM data quality, because the data were edited by global volunteers and they have only been assessed at a city/regional scale rather than at a global scale. This study assesses the quality of OSM-LCLU data for 168 countries worldwide. OSM-based LC datasets are firstly produced for different countries by referring to global open LC data, and these dataset are then compared in terms of accuracy and completeness. Moreover, a number of variables and three regression models (OLS, SLM and SEM) are used to understand the accuracy and completeness patterns at a global scale. We found that: 1) although most countries are characterized by a low completeness, they have a relatively high accuracy. 2) Both socio-economic variables and the area percentages of various OSM-LCLU types have been found to be significantly correlated with these patterns. 3) The SLM and SEM models are preferred, and most of countries with a relatively high completeness are spatially aggregated in Europe. Not only the global pattern of the OSM-LCLU data quality have been recovered, but also the analytical method can be applied to different countries and regions.

A CONCEPTUAL MODEL FOR CONVERTING OPENSTREETMAP CONTRIBUTION TO GEOSPATIAL MACHINE LEARNING TRAINING DATA

Abstract. In the recent decade, Volunteered Geographical Information (VGI), in particular the OpenStreetMap (OSM), has helped to fill substantial data gaps in base maps, especially in Global South, thus has become a promising source of massive, free training data together with rich and detailed semantic information for geospatial artificial intelligence (GeoAI) applications. Although intensive works have explored the potential of generating training data from OSM, a systematic approach of harvesting OSM contribution as quality-aware training data for different GeoAI tasks is still missing. To fill this research gap, we proposed a conceptual model consisting of three major components: historical OSM and external datasets, quality indicators, and GeoAI models. As a proof of concept, we validated our conceptual model with an example task of detecting OSM missing buildings in Mozambique, where the impact of different error sources (e.g., completeness, alignment, rotation) in training data were compared and investigated in a quantitative manner. The lessons learned in this paper shed important lights on cooperating OSM data quality aspects with the development of more explainable GeoAI models.

OSM Science—The Academic Study of the OpenStreetMap Project, Data, Contributors, Community, and Applications

This paper is an Editorial for the Special Issue titled “OpenStreetMap as a multidisciplinary nexus: perspectives, practices and procedures”. The Special Issue is largely based on the talks presented in the 2019 and 2020 editions of the Academic Track at the State of the Map conferences. As such, it represents the most pressing and relevant issues and topics considered by the academic community in relation to OpenStreetMap (OSM)—a global project and community aimed to create and maintain a free and editable database and map of the world. In this Editorial, we survey the papers included in the Special Issue, grouping them into three research perspectives: applications of OSM for studies within other disciplines, OSM data quality, and dynamics in OSM. This survey reveals that these perspectives, while being distinct, are also interrelated. This calls for the formalization of an ‘OSM science’ that will provide the conceptual grounds to advance the scientific study of OSM, not as a set of individualized efforts but as a unified approach.

Analysis of OpenStreetMap Data Quality for Selected Counties in Poland in Terms of Sustainable Development

One potential source of geospatial open data for monitoring sustainable development goals (SDG) indicators is OpenStreetMap (OSM). The purpose of this paper is to provide a comprehensive evaluation of the spatial data quality elements of OSM against the national official data—the database of topographic objects at a scale of 1:10,000. Such spatial data quality elements as location accuracy, data completeness and attribute compatibility were analysed. In the conducted OpenStreetMap tests, basic land-cover classes such as roads, railroads, river network, buildings, surface waters and forests were analysed. The test area of the study consisted of five counties in Poland, which differ in terms of location, relief, surface area and degree of urbanization. The best results of the quality of OSM spatial data were obtained for highly urbanized areas with developed infrastructure and a high degree of affluence. The highest degree of completeness of OSM linear and area objects in the studied counties was acquired in Piaseczyński County (82%). The lowest degree of completeness of the line and area objects of OSM in the studied counties was obtained in the Ostrowski County (51%). The calculated correlation coefficient between the quality of OSM data and the income per capita in the county was 0.96. The study complements the previous research results in the field of quantitative analysis of the quality of OSM data, and the obtained results confirm their dependence on the geometric type of the analysed objects and characteristics of test areas, i.e., in this case counties in Poland. The obtained results of OSM data quality analysis indicate that OSM data may provide strong support for other spatial data, including official and state data. OSM stores significant amounts of geospatial data with relatively high data quality that can be a valuable source for monitoring some SDG indicators.

Assessing OSM building completeness using population data

OpenStreetMap (OSM) is currently an important source for building data, despite the existence of potential quality issues. Previous studies have assessed OSM data quality by comparing it with reference building data, which may not otherwise be readily available. This study assessed OSM building completeness using population data, and investigated the effectiveness of using population data for building reference data. We proposed various approaches, including type-based and regression-based approaches and their subtypes, and designed measures and methods to evaluate these approaches. Our evaluation examined four study areas in two countries, using global population data sets at three spatial resolutions (1-km, 100-m, and 30-m). Results showed that the type-based approach correctly classified approximately 80–99% of the assessed grid cells. The regression-based approach resulted in a high linear correlation (0.7 or greater) between the population counts and the referenced building count/building area size, with the strongest correlation present for the 1-km population dataset. We conclude that the use of population data as referenced building data is an effective method for the assessment of OSM building completeness. The paper concludes with the advantages and limitations of using both the type-based and the regression-based approaches.

Can you fixme? An intrinsic classification of contributor-identified spatial data issues using topic models

ABSTRACT Assessing OpenStreetMap (OSM) data quality against authoritative data sources may not always be viable. This is primarily because of the multi-dimensional nature and heterogeneity of the maps, yet the activity is pivotal for targeted data cleansing and quality enhancement undertakings in these data sets. A salient facet of OSM, allowing contributors to flag potential problems encountered during the mapping process, is the FIXME tag. In this article, we examine and discuss OSM data quality through the vast expanse of issues (knowledge) documented via FIXME. We present a classification and analysis of these quality issues, exposed as topic models and grounded in the ISO-19157 standard, across USA and Australia. Regional distributions of these topics are further qualitatively analyzed to ascertain the variation of key issues in OSM. We also present a comparison of the intrinsic issue classification against those identified in an issue corpus of an authoritative map data source. Due to the considerable heterogeneity in user mapping and reporting, OSM issue detection and classification remains problematic. This research presents a flexible and intrinsic data-mining approach, linking established ISO data quality standards to OSM issue categorization. Our work, thus informs the development of automated error correction methods for VGI datasets.

A Contributor-Focused Intrinsic Quality Assessment of OpenStreetMap in Mozambique Using Unsupervised Machine Learning

Anyone can contribute geographic information to OpenStreetMap (OSM), regardless of their level of experience or skills, which has raised concerns about quality. When reference data is not available to assess the quality of OSM data, intrinsic methods that assess the data and its metadata can be used. In this study, we applied unsupervised machine learning for analysing OSM history data to get a better understanding of who contributed when and how in Mozambique. Even though no absolute statements can be made about the quality of the data, the results provide valuable insight into the quality. Most of the data in Mozambique (93%) was contributed by a small group of active contributors (25%). However, these were less active than the OSM Foundation’s definition of active contributorship and the Humanitarian OpenStreetMap Team (HOT) definition for intermediate mappers. Compared to other contributor classifications, our results revealed a new class: contributors who were new in the area and most likely attracted by HOT mapping events during disaster relief operations in Mozambique in 2019. More studies in different parts of the world would establish whether the patterns observed here are typical for developing countries. Intrinsic methods cannot replace ground truthing or extrinsic methods, but provide alternative ways for gaining insight about quality, and they can also be used to inform efforts to further improve the quality. We provide suggestions for how contributor-focused intrinsic quality assessments could be further refined.

Using OpenStreetMap as a Data Source for Attractiveness in Travel Demand Models

We present a methodology to extract points of interest (POIs) data from OpenStreetMap (OSM) for application in travel demand models. We use custom taglists to identify and assign POI elements to typical activities used in travel demand models. We then compare the extracted OSM data with official sources and point out that the OSM data quality depends on the type of POI and that it generally matches the quality of official sources. It can therefore be used in travel demand models. However, we recommend that plausibility checks should be done to ensure a certain quality. Further, we present a methodology for calculating attractiveness measures for typical activities from single POIs and national trip generation guidelines. We show that the quality of these calculated measures is good enough for them to be used in travel demand models. Using our approach, therefore, allows the quick, automated, and flexible generation of attractiveness measures for travel demand models.

Exploration of OpenStreetMap missing built-up areas using twitter hierarchical clustering and deep learning in Mozambique

Accurate and detailed geographical information digitizing human activity patterns plays an essential role in response to natural disasters. Volunteered geographical information, in particular OpenStreetMap (OSM), shows great potential in providing the knowledge of human settlements to support humanitarian aid, while the availability and quality of OSM remains a major concern. The majority of existing works in assessing OSM data quality focus on either extrinsic or intrinsic analysis, which is insufficient to fulfill the humanitarian mapping scenario to a certain degree. This paper aims to explore OSM missing built-up areas from an integrative perspective of social sensing and remote sensing. First, applying hierarchical DBSCAN clustering algorithm, the clusters of geo-tagged tweets are generated as proxies of human active regions. Then a deep learning based model fine-tuned on existing OSM data is proposed to further map the missing built-up areas. Hit by Cyclone Idai and Kenneth in 2019, the Republic of Mozambique is selected as the study area to evaluate the proposed method at a national scale. As a result, 13 OSM missing built-up areas are identified and mapped with an over 90% overall accuracy, being competitive compared to state-of-the-art products, which confirms the effectiveness of the proposed method.

Spatial-temporal Evolution and Completeness Analysis of OpenStreetMap Building Data in China from 2012 to 2017

Abstract. OpenStreetMap (OSM), as a typical volunteered geographic information project, is an online map with free content and everyone can edit and use it (Goodchild 2007). A range of applications has been proposed using OSM data, including routing and navigation, crisis mapping, 3D modelling, land use/cover mapping. This is because the OSM data is not only free of use, but also has a global coverage and high currentness. In despite of the above advantages, however, most of the OSM data were contributed by ‘non-professional’ or ‘amateur geographers’ (Goodchild 2008; Haklay 2010). Therefore, a lot of concerns have been paid attention to the quality issue of the OSM data. Assessing the quality of OSM data has become a hot topic in the field of geographic information science.Extensive studies have been carried out on assessing various quality measures (e.g. positional accuracy, completeness and attribute accuracy) of OSM datasets in different countries or districts such as Germany, England, France, Italy, Canada and the United States. In the meanwhile, the road feature of an OSM dataset has been paid much attention to. To our knowledge, however, not any study has been focused on assessing the data quality of OSM building data in China, although it may be an essential data source for urban planning and management, 3D modelling and indoor navigation. Therefore, the aim of this study is to investigate the OSM building data in China. More precisely, an analysis of the spatial-temporal evolution and completeness of OSM building data in China from 2012 to 2017 was carried out. The tenet of our study was to employ two quality indicators, i.e. building count (Gröchenig et al. 2013, Barron et al. 2014, Fan 2016) and building density (Zhou 2018), for the analyses. First, the numbers of OSM building data from 2012 to 2017 were calculated in terms of both provincial- and prefecture-level divisions in China; The OSM building count were then compared among different divisions and also different years (2012–2017) for analyzing the evolution of OSM building data in China in both temporal dimension and spatial scale. Moreover, the correlations between OSM building counts and four potential factors (i.e. gross domestic product (GDP), population, urban land area, OSM road length), which may influence the development of OSM building data in China, were respectively investigated. Second, a 1 × 1 km2 regular grid was overlapped onto the OSM building datasets in urban areas for calculating the OSM building density of each grid cell; Moreover, high-density grid cells (whose OSM building data were almost complete) were extracted and analyzed through a simple clustering method, in order to investigate the spatial pattern of OSM building data in urban areas. Results showed that,1) The OSM building data in China increased almost 20 times from the years 2012 to 2017, especially for those located in the eastern coastal regions of China (e.g. the provincial-level divisions: Jinagsu, Zhejiang, Guangzhou and Shandong and the prefecture-level divisions: Beijing, Nantong, Shanghai, Tianjin, Suzhou, Yangzhou and Dalian). In most cases, both the GDP and OSM road length factors had a moderate correlation with the OSM building count. 2) Most of the grid cells in urban areas still had no building or their building densities were equal to 0%, which indicated that the OSM building dataset in China was far from being complete. From analyzing the high density grid cells, two typical spatial distribution modes, i.e. dispersion and aggregation, were found in different prefecture-level divisions. As an example, the high-density grid cells for some prefecture-level divisions (e.g. Luoyang, Yueyang and Dalian) were mostly aggregated in the city cores; while those for some (e.g. Beijing Tianjin and Shanghai) were located in the hot spots such as business districts, attractions and transportation hubs.The above results may benefit for users (especially those researchers and educators) to choose appropriate study area(s) from the OSM building dataset in China. In the meanwhile, the volunteers around the world may be motivated to contribute more OSM building data in this region. Further research work may include: developing quality indicators for quantitative completeness estimation of OSM building data, especially in rural areas; and investigating other quality measures (e.g. positional accuracy and semantic accuracy) or geographical features (e.g. railways, land uses, and points of interest) in China’s OSM dataset.

OSHDB: a framework for spatio-temporal analysis of OpenStreetMap history data

OpenStreetMap (OSM) is a collaborative project collecting geographical data of the entire world. The level of detail of OSM data and its data quality vary much across different regions and domains. In order to analyse such variations it is often necessary to research the history and evolution of the OSM data.The OpenStreetMap History Database (OSHDB) is a new data analysis tool for spatio-temporal geographical vector data. It is specifically optimized for working with OSM history data on a global scale and allows one to investigate the data evolution and user contributions in a flexible way. Benefits of the OSHDB are for example: to facilitate accessing OSM history data as a research subject and to assess the quality of OSM data by using intrinsic measures.This article describes the requirements of such a system and the resulting technical implementation of the OSHDB: the OSHDB data model and its application programming interface.

Automatic analysis of positional plausibility for points of interest in OpenStreetMap using coexistence patterns

ABSTRACTIn the past decade, Volunteered Geographic Information (VGI) has emerged as a new source of geographic information, making it a cheap and universal competitor to existing authoritative data sources. The growing popularity of VGI platforms, such as OpenStreetMap (OSM), would trigger malicious activities such as vandalism or spam. Similarly, wrong entries by unexperienced contributors adds to the complexities and directly impact the reliability of such databases. While there are some existing methods and tools for monitoring OSM data quality, there is still a lack of advanced mechanisms for automatic validation. This paper presents a new recommender tool which evaluates the positional plausibility of incoming POI registrations in OSM by generating near real-time validation scores. Similar to machine learning techniques, the tool discovers, stores and reapplies binary distance-based coexistence patterns between one specific POI and its surrounding objects. To clarify the idea, basic concepts about analysing coexistence patterns including design methodology and algorithms are covered in this context. Furthermore, the results of two case studies are presented to demonstrate the analytical power and reliability of the proposed technique. The encouraging results of this new recommendation tool elevates the need for developing reliable quality assurance systems in OSM and other VGI projects.

Analyzing OpenStreetMap Road Data and Characterizing the Behavior of Contributors in Ankara, Turkey

The usage of OpenStreetMap (OSM), one of the resources offered by Volunteered Geographic Information (VGI), has rapidly increased since it was first established in 2004. In line with this increased usage, a number of studies have been conducted to analyze the accuracy and quality of OSM data, but many of them have constraints on evaluating the profiles of contributors. In this paper, OSM road data have been analyzed with the aim of characterizing the behavior of OSM contributors. The study area, Ankara, the capital city of Turkey, was evaluated with several network analysis methods, such as completeness, degree of centrality, betweenness, closeness, PageRank, and a proposed method measuring the activation of contributors in a bounded area from 2007–2017. An evaluation of the results was also discussed in this paper by taking into account the following indicators for each year: number of nodes, ways, contributors, mean lengths, and sinuosity values of roads. The results show that the experience levels of the contributors determine the contribution type. Essentially, more experience makes for more detailed contributions.

THE OPINION OF CROWD PARTICIPATORY FOR OPENSTREETMAP: A SURVEY IN CHINA

Abstract. At the present, OpenStreetMap (OSM) is considered as one of the most successful and popular VGI (Volunteered Geographic Information) projects. It provides a platform that all the registered members coming from different areas in the world can cooperate with each other to mapping our world. Besides, OSM attracts more and more people, companies and even the governmental agencies because of its free and open source. Studies have proofed that both the quantity and quality of OSM data in several western countries, i.e. Germany, France and the Netherland are even better than the authority data. In recent years, the quantity of the OSM data and the number of contributors in China increased rapidly, but the overall distribution of OSM data is very fit with the distribution of population and the economic development and it displays an uneven development in different provinces and cities in China. Besides, the state of the OSM in China is just similar to that in Germany in 2010 in terms of data quantity and quality, although China is about 25 times to Germany regarding land area and the smartphone penetration in China and Germany does not have a large distance (51.7 % to 68.8 %). Why is the development of OSM in China so poor and backward when comparing that with western countries, although the environment in hardware and software in China are similar to the western countries? Attempting to answer this question, this paper presents a user survey in China. Mainly, knowledges and experiences about OSM and OSM contribution were asked in the user survey. The user survey was conducted both by paper and pen and by using online platform. Totally, over 1200 participants with the age range from 15 to 80 and a huge diverse of background took part in the user survey. In this paper, we would like to describe the design of the questions for the user survey at first. Then we will demonstrate the results of the user survey, as well as the analysis and conclusions, which can be drawn from the user survey.

Assessing OpenStreetMap Data Using Intrinsic Quality Indicators: An Extension to the QGIS Processing Toolbox

OpenStreetMap (OSM) is a recent emerging area in computational science. There are several unexplored issues in the quality assessment of OSM. Firstly, researchers are using various established assessment methods by comparing OSM with authoritative dataset. However, these methods are unsuitable to assess OSM data quality in the case of the non-availability of authoritative data. In such a scenario, the intrinsic quality indicators can be used to assess the quality. Secondly, a framework for data assessment specific to different geographic information system (GIS) domains is not available. In this light, the current study presents an extension of the Quantum GIS (QGIS) processing toolbox by using existing functionalities and writing new scripts to handle spatial data. This would enable researchers to assess the completeness of spatial data using intrinsic indicators. The study also proposed a heuristic approach to test the road navigability of OSM data. The developed models are applied on Punjab (India) OSM data. The results suggest that the OSM project in Punjab (India) is progressing at a slow peace, and contributors’ motivation is required to enhance the fitness of data. It is concluded that the scripts developed to provide an intuitive method to assess the OSM data based on quality indicators can be easily utilized for evaluating the fitness-of-use of the data of any region.

Outlier Detection in OpenStreetMap Data using the Random Forest Algorithm

GIScience 2016 Short Paper Proceedings Outlier Detection in OpenStreetMap Data using the Random Forest Algorithm Richard Wen, Claus Rinner Department of Geography and Environmental Studies, Ryerson University 350 Victoria St., Toronto, Ontario, M5B 2K3, Canada Email: {rwen, crinner}@ryerson.ca Abstract OpenStreetMap (OSM) data consist of digitized geographic objects with semantic tags assigned by the volunteer contributors. The tags describe the geographic objects in a way that is understandable by both humans and computers. The variability in contributor behaviour creates reliability concerns for the tagging quality of OSM data. The detection of irregular contributions may improve OSM data quality and editing tools. This research applies the random forest algorithm on geospatial variables in order to detect outliers without ground-truth reference data to direct human inspection. An application to OSM data for Toronto, Ontario, was effective in revealing abnormal amenity tagging of school and hospital objects. 1. Introduction OpenStreetMap (OSM) is an online platform enabling registered volunteers to contribute geospatial data by digitizing point-, line-, or polygon-shaped geographic objects and annotating them with tags referring to common feature classes such as roads and restaurants (Haklay 2008). OSM tags are semantically structured as key-value pairs, where the key refers to a broad class of geographic objects and the value details the specific geographic object being tagged (Ballatore et al. 2013). Examples of tags are amenity=school, highway=residential, and building=house. The open and flexible nature of OSM tagging leads to varying contribution behaviour by different communities (Mooney et al. 2010). The varying contribution behaviour creates concerns about the quality of OSM data and the community standards of OSM tagging. Quality control and corrections rely heavily on human interaction, which raises additional questions on the reliability of OSM data. Finally, the experience of the volunteer contributor has an effect on the tagging quality of each geographic object as experienced contributors are more familiar with the tagging norms of the area being edited. Although OSM is an effective and efficient platform for generating masses of geospatial data, it is plagued by reliability, quality, and completeness issues. The aim of this paper is to examine the ability of an automated machine learning algorithm, the random forest algorithm, to support manual human inspection and minimize bias in OSM data editing. The use of an automated algorithm improves the detection of abnormal tagging behaviour, avoids the bias of human judgement, and reduces the time required to search through masses of tagged geographic objects. A combination of human knowledge and experience with the logical accuracy of machines could improve OSM tagging quality and standards, and enable the development of advanced editing tools. 2. Data and Methods

Areal Delineation of Home Regions from Contribution and Editing Patterns in OpenStreetMap

The type of data an individual contributor adds to OpenStreetMap (OSM) varies by region. The local knowledge of a data contributor allows for the collection and editing of detailed features such as small trails, park benches or fire hydrants, as well as adding attribute information that can only be accessed locally. As opposed to this, satellite imagery that is provided as background images in OSM data editors, such as ID, Potlatch or JOSM, facilitates the contribution of less detailed data through on-screen digitizing, oftentimes for areas the contributor is less familiar with. Knowing whether an area is part of a contributor’s home region or not can therefore be a useful predictor of OSM data quality for a geographic region. This research explores the editing history of nodes and ways for 13 highly active OSM members within a two-tiered clustering process to delineate an individual mapper’s home region from remotely mapped areas. The findings are evaluated against those found with a previously introduced method which determines a contributor’s home region solely based on spatial clustering of created nodes. The comparison shows that both methods are able to delineate similar home regions for the 13 contributors with some differences.

Updating digital elevation models via change detection and fusion of human and remote sensor data in urban environments

OpenStreetMap (OSM) currently represents the most popular project of Volunteered Geographic Information (VGI): geodata are collected by common people and made available for public use. Airborne Laser Scanning (ALS) enables the acquisition of high-resolution digital elevation models that are used for many applications. This study combines the advantages of both ALS and OSM, offering a promising new approach that enhances data quality and allows change detection: the mainly up-to-date 2D data of OSM can be combined with the high-resolution – but rarely updated – elevation information provided by ALS. This case study investigates building objects of OSM and ALS data of the city of Bregenz, Austria. Data quality of OSM is discerned by the comparison of building footprints using different true positive definitions (e.g. overlapping area). High quality of OSM data is revealed, yet also limitations of each method with respect to heterogeneous regions and building outlines are identified. For the first time, an up-to-date Digital Surface Model (DSM) combining 2D OSM and ALS data is achieved. A multitude of applications such as flood simulations and solar potential assessments can directly benefit from this data combination, since their value and reliability strongly depend on an up-to-date DSM.

The Annotation Process in OpenStreetMap

AbstractIn this article we describe the analysis of 25,000 objects from the OpenStreetMap (OSM) databases of Ireland, United Kingdom, Germany, and Austria. The objects are selected as exhibiting the characteristics of “heavily edited” objects. We consider “heavily edited” objects as having 15 or more versions over the object's lifetime. Our results indicate that there are some serious issues arising from the way contributors tag or annotate objects in OSM. Values assigned to the “name” and “highway” attributes are often subject to frequent and unexpected change. However, this “tag flip‐flopping” is not found to be strongly correlated with increasing numbers of contributors. We also show problems with usage of the OSM ontology/controlled vocabularly. The majority of errors occurring were caused by contributors choosing values from the ontology “by hand” and spelling these values incorrectly. These issues could have a potentially detrimental effect on the quality of OSM data while at the same time damaging the perception of OSM in the GIS community. The current state of tagging and annotation in OSM is not perfect. We feel that the problems identified are a combination of the flexibility of the tagging process in OSM and the lack of a strict mechanism for checking adherence to the OSM ontology for specific core attributes. More studies related to comparing the names of features in OSM to recognized ground‐truth datasets are required.