Are crowdsourced datasets suitable for specialized routing services? Case study of Openstreetmap for routing of people with limited mobility

Nowadays, Volunteered Geographic Information (VGI) has increasingly gained attractiveness to both amateur users and professionals. Using data generated from the crowd has become a hot topic for several application domains including transportation. However, there are concerns regarding the quality of such datasets. As one of the most famous crowdsourced mapping platforms, we analyze the fitness for use of OpenStreetMap (OSM) database for routing and navigation of people with limited mobility. We assess the completeness of OSM data regarding sidewalk information. Relevant attributes for sidewalk information such as sidewalk width, incline, surface texture, etc. are considered, and through both extrinsic and intrinsic quality analysis methods, we present the results of fitness for use of OSM data for routing services of disabled persons. Based on empirical results, it is concluded that OSM data of relatively large spatial extents inside all studied cities could be an acceptable region of interest to test and evaluate wheelchair routing and navigation services, as long as other data quality parameters such as positional accuracy and logical consistency are checked and proved to be acceptable. We present an extended version of OSMatrix web service and explore how it is employed to perform spatial and temporal analysis of sidewalk data completeness in OSM. The tool is beneficial for piloting activities, whereas the pilot site planners can query OpenStreetMap and visualize the degree of sidewalk data availability in a certain region of interest. This would allow identifying the areas that data are mostly missing and plan for data collection events. Furthermore, empirical results of data completeness for several OSM data indicators and their potential relation to sidewalk data completeness are presented and discussed. Finally, the article ends with an outlook for future research study in this area

Land-cover change detection using paired OpenStreetMap data and optical high-resolution imagery via object-guided Transformer

Optical high-resolution imagery and OpenStreetMap (OSM) data are two important data sources for land-cover change detection. Previous studies in these two data sources focus on utilizing the information in OSM data to aid the change detection on multi-temporal optical high-resolution images. This paper pioneers the direct detection of land-cover changes utilizing paired OSM data and optical imagery, thereby broadening the horizons of change detection tasks to encompass more dynamic earth observations. To this end, we propose an object-guided Transformer (ObjFormer) architecture by naturally combining the prevalent object-based image analysis (OBIA) technique with the advanced vision Transformer architecture. The introduction of OBIA can significantly reduce the computational overhead and memory burden in the self-attention module. Specifically, the proposed ObjFormer has a hierarchical pseudo-siamese encoder consisting of object-guided self-attention modules that extract representative features of different levels from OSM data and optical images; a decoder consisting of object-guided cross-attention modules can progressively recover the land-cover changes from the extracted heterogeneous features. In addition to the basic supervised binary change detection task, this paper raises a new semi-supervised semantic change detection task that does not require any manually annotated land-cover labels of optical images to train semantic change detectors. Two lightweight semantic decoders are added to ObjFormer to accomplish this task efficiently. A converse cross-entropy loss is designed to fully utilize the negative samples, thereby contributing to the great performance improvement in this task. The first large-scale benchmark dataset containing 1,287 map-image pairs (1024$\times$ 1024 pixels for each sample) covering 40 regions on six continents ...(see the manuscript for the full abstract

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

15-06 Integrated Crowdsourcing Platform to Investigate Non-Motorized Behavior and Risk Factors on Walking, Running, and Cycling Routes

There are several factors on the roads that impact bicyclists’ safety. This research aims to find the most important risk factors on roads, mainly in infrastructure facilities, to improve the safety for walkers, runners, and bicyclists. Most mobile cycling applications currently used by cyclists and runners were reviewed in this study in order to gain insight about the features that users care about. Features, such as speed, cumulative elevation gain, and connectivity to Google Fit, were found to be the most common features in the widely-used cycling apps. In this research, we developed and launched a mobile application for crowd-sourcing of roads’ risk factors. With the proposed application, some of the cycling risk factors can be mitigated. We launched the BikeableRoute mobile application allowing bicyclists to share reports of hazards encountered on roads with other fellow bicyclists and the local authorities. To achieve the goals of this study, the mobile application collects anonymous data and self-reported risk factors and biking data. This study allows collecting user’s data for later processing to extract knowledge and insight. Our proposed system enables local authorities to operate more efficiently to handle the feedback provided by the citizens. Also, the local government will be able to provide statistical reports that provide estimates of the traffic on the different routes throughout the local community

Assessing the accuracy of openstreetmap data in south africa for the purpose of integrating it with authoritative data

Includes bibliographical references.The introduction and success of Volunteered Geographic Information (VGI) has gained the interest of National Mapping Agencies (NMAs) worldwide. VGI is geographic information that is freely generated by non-experts and shared using VGI initiatives available on the Internet. The NMA of South Africa i.e. the Chief Directorate: National Geo- Spatial Information (CD: NGI) is looking to this volunteer information to maintain their topographical database; however, the main concern is the quality of the data. The purpose of this work is to assess whether it is feasible to use VGI to update the CD: NGI topographical database. The data from OpenStreetMap (OSM), which is one the most successful VGI initiatives, was compared to a reference data set provided by the CD: NGI. Corresponding features between the two data sets were compared in order to assess the various quality aspects. The investigation was split into quantitative and qualitative assessments. The aim of the quantitative assessments was to determine the internal quality of the OSM data. The internal quality elements included the positional accuracy, geometric accuracy, semantic accuracy and the completeness. The _rst part of the qualitative assessment was concerned with the currency of OSM data between 2006 and 2012. The second part of the assessment was focused on the uniformity of OSM data acquisition across South Africa. The quantitative results showed that both road and building features do not meet the CD: NGI positional accuracy standards. In some areas the positional accuracy of roads are close to the required accuracy. The buildings generally compare well in shape to the CD: NGI buildings. However, there were very few OSM polygon features to assess, thus the results are limited to a small sample. The semantic accuracy of roads was low. Volunteers do not generally classify roads correctly. Instead, many volunteers prefer to class roads generically. The last part of the quantitative results, the completeness, revealed that commercial areas reach high completeness percentages and sometimes exceed the total length of the CD: NGI roads. In residential areas, the percentages are lower and in low urban density areas, the lowest. Nonetheless, the OSM repository has seen signi_cant growth since 2006. The qualitative results showed that because the OSM repository has continued to grow since 2006, the level of currency has increased. In South Africa, the most contributions were made between 2010 and 2012. The OSM data set is thus current after 2012. The amount and type of contributions are however not uniform across the country for various reasons. The number of point contributions was low. Thus, the relationship between the type of contribution and the settlement type could not be made with certainty. Because the OSM data does not meet the CD: NGI spatial accuracy requirements, the two data sets cannot be integrated at the database level. Instead, two options are proposed. The CD: NGI could use the OSM data for detecting changes to the landscape only. The other recommendation is to transform and verify the OSM data. Only those features with a high positional accuracy would then be ingested. The CD: NGI currently has a shortage of sta_ that is quali_ed to process ancillary data. Both of the options proposed thus require automated techniques because it is time consuming to perform these tasks manually

Exploring the Potential of Machine and Deep Learning Models for OpenStreetMap Data Quality Assessment and Improvement (Short Paper)

The OpenStreetMap (OSM) project is a widely-used crowdsourced geographic data platform that allows users to contribute, edit, and access geographic information. However, the quality of the data in OSM is often uncertain, and assessing the quality of OSM data is crucial for ensuring its reliability and usability. Recently, the use of machine and deep learning models has shown to be promising in assessing and improving the quality of OSM data. In this paper, we explore the current state-of-the-art machine learning models for OSM data quality assessment and improvement as an attempt to discuss and classify the underlying methods into different categories depending on (1) the associated learning paradigm (supervised or unsupervised learning-based methods), (2) the usage of extrinsic or intrinsic-based metrics (i.e., assessing OSM data by comparing it against authoritative external datasets or via computing some internal quality indicators), and (3) the use of traditional or deep learning-based models for predicting and evaluating OSM features. We then identify the main trends and challenges in this field and provide recommendations for future research aiming at improving the quality of OSM data in terms of completeness, accuracy, and consistency

How Good Is Open Bicycle Infrastructure Data? A Countrywide Case Study of Denmark

Cycling is a key ingredient for a sustainability shift of Denmark's transportation system. To increase cycling rates, a better nationwide network of bicycle infrastructure is required. Planning such a network requires high-quality infrastructure data, however, the quality of bicycle infrastructure data is severely understudied. Here, we compare Denmark's two largest open data sets on dedicated bicycle infrastructure, OpenStreetMap (OSM) and GeoDanmark, in a countrywide data quality assessment, asking whether data is good enough for network-based analysis of cycling conditions. We find that neither of the data sets is of sufficient quality, and that data set conflation is necessary to obtain a complete dataset. Our analysis of the spatial variation of data quality suggests that rural areas are more likely to suffer from problems with data completeness. We demonstrate that the prevalent method of using infrastructure density as a proxy for data completeness is not suitable for bicycle infrastructure data, and that matching of corresponding features thus is necessary to assess data completeness. Based on our data quality assessment we recommend strategic mapping efforts towards data completeness, consistent standards to support comparability between different data sources, and increased focus on data topology to ensure high-quality bicycle network data

osmdata

Download and import of "OpenStreetMap" ('OSM') data as 'sf' or 'sp'objects. 'OSM' data are extracted from the 'Overpass' web server and processed with very fast 'C++' routines for return to 'R'

So2Sat POP -- A Curated Benchmark Data Set for Population Estimation from Space on a Continental Scale

Obtaining a dynamic population distribution is key to many decision-making processes such as urban planning, disaster management and most importantly helping the government to better allocate socio-technical supply. For the aspiration of these objectives, good population data is essential. The traditional method of collecting population data through the census is expensive and tedious. In recent years, machine learning methods have been developed to estimate the population distribution. Most of the methods use data sets that are either developed on a small scale or not publicly available yet. Thus, the development and evaluation of the new methods become challenging. We fill this gap by providing a comprehensive data set for population estimation in 98 European cities. The data set comprises digital elevation model, local climate zone, land use classifications, nighttime lights in combination with multi-spectral Sentinel-2 imagery, and data from the Open Street Map initiative. We anticipate that it would be a valuable addition to the research community for the development of sophisticated machine learning-based approaches in the field of population estimation