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OpenStreetMap (OSM) as one of the most successful projects of Volunteered Geographical Information (VGI) has attracted millions of contributors to work together and produces massive open geographical data. However, the co-work does not always run smoothly since mapping can involve conflicted understandings of the reality. In this paper, we investigate behaviors of mapping related to territorial disputes to reveal the characteristics of contributions and examine the contradictions between ground truth as the vision of OSM and the theory of critical cartography. We perform our experiments from the perspectives of entities, changesets, and contributors using the full history data of OSM. The experiments show that territorial-dispute-related contributions have substantially different characteristics from various aspects but they cannot be treated as outliers either, considering that most contributors do not focus on disputed boundaries. Interpreting OSM data as a converging state to ground truth or equally opinions can both be inaccurate. We also find that mapping disputes may not be absolutely negative in a VGI project. We perform quantitative, large-scale (global) analysis of dispute-related mapping. The results show that territorial-dispute-related contributions and contributors are different from contributions and contributors in general. Territorial-dispute-related mapping is not an independent phenomenon for OSM. The contributors make much more disputes-unrelated contributions. Dispute-related entities have more (divergent) versions than normal boundaries, attract more participants, and are more semantically complete, especially for names. Dispute-related changesets generally attract more discussions. The spatial distribution of the dispute-related changesets is consistent with real-world territorial disputes and very different from that of all boundary-related changesets and all changesets. Contributors who participate in dispute-related contributions are generally more active. These users tend to have a special interest in boundaries but most of them do not focus on disputed boundaries. [ABSTRACT FROM AUTHOR]

Extracting precise location information from microblogs is a crucial task in many applications, particularly in disaster response, revealing where damages are, where people need assistance, and where help can be found. A crucial prerequisite to location extraction is place name extraction. In this paper, we present GazPNE: a hybrid approach to place name extraction which fuses rules, gazetteers, and deep learning techniques without requiring any manually annotated data. The core of the approach is to learn the intrinsic characteristics of multi-word place names with deep learning from gazetteers. Specifically, GazPNE consists of a rule-based system to select n-grams from the microblogs that potentially contain place names, and a C-LSTM model that decides if the selected n-gram is a place name or not. The C-LSTM is trained on 388.1 million examples containing 6.8 million positive examples with US and Indian place names extracted from OpenStreetMap and 381.3 million negative examples synthesized by rules. We evaluate GazPNE against the SoTA on a manually annotated 4,500 tweet dataset which contains 9,026 place names from three foods: 2016 in Louisiana (US), 2016 in Houston (US), and 2015 in Chennai (India). GazPNE achieves SotA performance on the test data with an F1 of 0.84. [ABSTRACT FROM AUTHOR]

Determining the location of a building's entrance is crucial to location-based services, such as wayfinding for pedestrians. Unfortunately, entrance information is often missing from current mainstream map providers such as Google Maps. Frequently, automatic approaches for detecting building entrances are based on street-level images that are not widely available. To address this issue, we propose a more general approach for inferring the main entrances of public buildings based on the association between spatial elements extracted from OpenStreetMap. In particular, we adopt three binary classification approaches, weighted random forest, balanced random forest, and smooth-boost to model the association relationship. There are two types of features considered in the classification: intrinsic features derived from building footprints and extrinsic features derived from spatial contexts, such as roads, green spaces, bicycle parking areas, and neighboring buildings. We conducted extensive experiments on 320 public buildings with an average perimeter of 350 m. The experimental results showed that the locations of building entrances estimated by the weighted random forest and balanced random forest models have a mean linear distance error of 21 m and a mean path distance error of 22 m, ruling out 90% of the incorrect locations of the main entrance of buildings. [ABSTRACT FROM AUTHOR]

Despite the rapid advancement and extensive applications of online Volunteered Geographical Information (VGI) projects such as OpenStreetMap (OSM), the persistence of data inequality remains a significant challenge, compromising the global reliability of their data products. This study examines the influence of contributions made by organizations, which have notably risen within the OSM community, on data inequality. The Gini coefficient is utilized to quantify data inequality, while a suite of statistical methods, including spectral analysis and robust correlation analysis, is applied to evaluate the distribution and impact of organizational efforts across various nations. Our findings indicate that organizations predominantly allocate their resources to nations with less complete data and surpass collective efforts of average contributors in mitigating OSM data inequality. Furthermore, the phenomena appears to be particularly significant for NGOs or corporations with humanitarian visions. • OpenStreetMap data exhibit significant inequality among countries, with a reduction within the span of our study (2015–2020). • Organizations contribute more to countries with incomplete data, reducing data inequality more effectively than the crowd. • Organizations with humanitarian visions prioritize countries with the most substantial data gaps. [ABSTRACT FROM AUTHOR]

Currently, very few roof shape information for complex buildings is available on OSM. Moreover, additional data requirements (e.g. 3D point clouds) limit the applicability of many roof reconstruction approaches. To mitigate this issue, we propose an approach to roof shape recommendations for complex buildings by exploring the inherited characteristics of building footprints: the disclosure of rectangles combinations in a partition of footprints and the symmetrical features of footprints. First, it decomposes a complex footprint into rectangles by using an advanced minimal non-overlapping cover algorithm. Second, a graph-based symmetry detection algorithm is proposed to identify all the symmetrical sub-clusters in partitions. Then, a set of selection rules are defined to rank partitions, and the best ones are chosen for roof shape recommendation. Finally, a set of combination rules and a symmetry rule are defined. It enables to evaluate the probability of a footprint being a certain combination of roof shapes. Experimental results show the growth of the probability of correctly recommending roof shapes for single rectangles and buildings from a prior probability of 17-45% and from a prior probability of 0.29-14.3%, removing 60% and 93% of the incorrect roof shape options, respectively. [ABSTRACT FROM AUTHOR]

Matching road networks is an essential step for data enrichment and data quality assessment, among other processes. Conventionally, road networks from two datasets are matched using a line-based approach that checks for the similarity of properties of line segments. In this article, a polygon-based approach is proposed to match the OpenStreetMap road network with authority data. The algorithm first extracts urban blocks that are central elements of urban planning and are represented by polygons surrounded by their surrounding streets, and it then assigns road lines to edges of urban blocks by checking their topologies. In the matching process, polygons of urban blocks are matched in the first step by checking for overlapping areas. In the second step, edges of a matched urban block pair are further matched with each other. Road lines that are assigned to the same matched pair of urban block edges are then matched with each other. The computational cost is substantially reduced because the proposed approach matches polygons instead of road lines, and thus, the process of matching is accelerated. Experiments on Heidelberg and Shanghai datasets show that the proposed approach achieves good and robust matching results, with a precision higher than 96% and a F1-score better than 90%. [ABSTRACT FROM PUBLISHER]

This study proposes a novel approach for extracting multilane roads from urban road networks in OpenStreetMap (OSM) data sets as functional high-level roads, thereby allowing comparative analyses to determine the differences between this functional hierarchy and other hierarchies. OSM road networks have high levels of detail and complex structures, but they also have large numbers of duplicated lines for the same road features, which leads to difficulties and low efficiency when extracting multilane roads using conventional methods based on the analysis and operations of line segments. To overcome these deficiencies, a polygon-based method is proposed that is based on shape analysis and Gestalt theory, which treats polygons surrounded by roads as operating elements. First, shape descriptors are calculated for each polygon in networks and are used for classification. Second, candidate multilane polygons are classified as seeds based on all the polygons used as shape descriptors by a support vector machine. Finally, based on the seed polygons, a region-growing method is proposed that connects and fills the multilane features according to Gestalt theory. An experiment using OSM data from different urban networks verified the validity of the proposed method. The method achieved good and effective extraction performance, regardless of the complexity and duplication of data sets. Thus, a comparative analysis with high-level roads extracted based on road type attributes and structural analysis was performed to demonstrate the differences between the constructed road levels and other hierarchies. [ABSTRACT FROM PUBLISHER]

In the past two years, several applications of generating three-dimensional (3D) buildings from OpenStreetMap (OSM) have been made available, for instance, OSM-3D, OSM2World, OSM Building, etc. In these projects, 3D buildings are reconstructed using the buildings’ footprints and information about their attributes, which are documented as tags in OSM. Therefore, the quality of 3D buildings relies strongly on the quality of the building footprints data in OSM. This article is dedicated to a quality assessment of building footprints data in OSM for the German city of Munich, which is one of the most developed cities in OSM. The data are evaluated in terms of completeness, semantic accuracy, position accuracy, and shape accuracy by using building footprints in ATKIS (German Authority Topographic–Cartographic Information System) as reference data. The process contains three steps: finding correspondence between OSM and ATKIS data, calculating parameters of the four quality criteria, and statistical analysis. The results show that OSM footprint data in Munich have a high completeness and semantic accuracy. There is an offset of about four meters on average in terms of position accuracy. With respect to shape, OSM building footprints have a high similarity to those in ATKIS data. However, some architectural details are missing; hence, the OSM footprints can be regarded as a simplified version of those in ATKIS data. [ABSTRACT FROM AUTHOR]