Crowdsourced geospatial data (CGD) is an important emerging trend that will influence future methods for geospatial data production and use. Related to broader developments in user-generated content, CGD involves the participation of end-users, many of who are untrained in the geospatial sciences but have a high degree of interest in geospatial technology. Working collectively, these end-users collect, edit, and produce datasets; create mapping applications, and develop tools for CGD.Crowdsourced geospatial data production is typically an open, lightly controlled process with few constraints, specifications, or quality assurance processes. This sharply contrasts with the less flexible and more controlled authoritative geospatial data production practices of national mapping agencies and businesses. Adoption of CGD and production methods has been a concern, especially to Government organizations, due quality concerns related to differences in production methods.We review CGD projects addressing common geospatial data collection tasks and demonstrating varied approaches to quality control, including hybrid projects that mix crowdsourced geospatial data and tools with authoritative data. The most common methods for quality assessment are summarized along with a comprehensive set of fitness-for-use considerations. Based on this information, lessons learned and future trends are summarized.

Disabled people, especially the blind and vision‐impaired, are challenged by many transitory hazards in urban environments such as construction barricades, temporary fencing across walkways, and obstacles along curbs. These hazards present a problem for navigation, because they typically appear in an unplanned manner and are seldom included in databases used for accessibility mapping. Tactile maps are a traditional tool used by blind and vision‐impaired people for navigation through urban environments, but such maps are not automatically updated with transitory hazards. As an alternative approach to static content on tactile maps, we use volunteered geographic information (VGI) and an Open Source system to provide updates of local infrastructure. These VGI updates, contributed via voice, text message, and e‐mail, use geographic descriptions containing place names to describe changes to the local environment. After they have been contributed and stored in a database, we georeference VGI updates with a detailed gazetteer of local place names including buildings, administrative offices, landmarks, roadways, and dormitories. We publish maps and alerts showing transitory hazards, including location‐based alerts delivered to mobile devices. Our system is built with several technologies including PHP, JavaScript, AJAX, Google Maps API, PostgreSQL, an Open Source database, and PostGIS, the PostgreSQL's spatial extension. This article provides insight into the integration of user‐contributed geospatial information into a comprehensive system for use by the blind and vision‐impaired, focusing on currently developed methods for geoparsing and georeferencing using a gazetteer.

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A A r re ep po or rt t o on n q qu ua al li it ty y a as ss se es ss sm me en nt t, , d dy yn na am mi ic c e ex xt te en ns si io on ns s, , a an nd d m mo ob bi il le e d de ev vi ic ce e e en ng ga ag ge em me en nt t i in n t th he e G Ge eo or rg ge e M Ma as so on n U Un ni iv ve er rs si it ty y G Ge eo oc cr ro ow wd ds so ou ur rc ci in ng g T Te es st tb be ed AbstractThis report documents the development of the George Mason University Geocrowdsourcing Testbed (GMU-GcT), whose purpose is to provide a platform for studying the dynamics, limitations, and best practices of geocrowdsourcing. We present a comprehensive study of the social moderation process in the GMU-GcT and the quality parameters of information in the GMU-GcT. We present an analysis of device-based positioning in mobile geocrowdsourcing, and use the study and our analysis as a context for dynamic application extensions of the GMU-GcT in the areas of field-based obstacle moderation, obstacle interaction, and accessible routing.

Geocrowdsourcing is a significant new focus area in mapping for people with disabilities. It utilizes public data contributions that are difficult to capture with traditional mapping workflows. Along with the benefits of geocrowdsourcing are critical drawbacks, including reliability and accuracy. A geocrowdsourcing testbed has been designed to explore the dynamics of geocrowdsourcing and quality assessment and produce temporally relevant navigation obstacle data. These reports are then used for route planning, obstacle avoidance, and spatial awareness. Recently, the geocrowdsourcing testbed has been modified to focus on the contribution of images and short descriptions, rather than the more lengthy previous reporting process. The quality assessment workflow of the geocrowdsourcing testbed is contrasted with a modified quality assessment workflow, implemented in the simpler and quicker image-based reporting paradigm. General quality assessment of data position and temporal characteristics is still possible, while general data attributes and detail are now supplied by a moderator from the contributed image. The derivation of obstacle location from multiple intersected image direction vectors does not produce reliable results, but an approach using buffered convex hulls works dependably. This simpler, quicker geocrowdsourcing workflow produces geocrowdsourced obstacle data and quality assessment estimates for location, time, and attribute accuracy.