Landmark-based navigation systems have proven benefits relative to traditional turn-by-turn systems that use street names and distances. However, one obstacle to the implementation of landmark-based navigation systems is the complex challenge of selecting salient local landmarks at each decision point for each user. In this paper, we present Pharos, a novel system that extends turn-by-turn navigation instructions using a single global landmark (e.g. the Eiffel Tower, the Burj Khalifa, municipal TV towers) rather than multiple, hard-to-select local landmarks. We first show that our approach is feasible in a large number of cities around the world through the use of computer vision to select global landmarks. We then present the results of a study demonstrating that by including global landmarks in navigation instructions, users navigate more confidently and build a more accurate mental map of the navigated area than using turn-by-turn instructions.
Detecting landmarks is a crucial step in the pipeline of all location-based services relying on landmarks. While researchers have often opted to perform the selection of landmarks by hand @cite_18 @cite_23 or using crowd-based @cite_55 approaches, commercially successful applications require a solution that works on a global scale across a large set of users. As such, some automated approaches have been developed that make use of large databases of geographic features (e.g. OpenStreetMap [OSM]) @cite_56 @cite_37 . For example, Raubal and Winter @cite_37 explored landmark-enriched wayfinding instructions by automatically extracting local landmarks from a spatial dataset based on a formal measure for landmark saliency. More recently, Dr "ager and Koller @cite_56 generated landmark-based navigation instructions for car navigation systems using OSM data. They used proximity to certain geographic features as a criterion to select landmarks. For prominent landmarks, the proximity can be used to estimate if a geotagged image shows a particular landmark @cite_44 .
@cite_18 @cite_37 @cite_55 @cite_56 @cite_44 @cite_23
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Deep Map is a research framework that aims at building the prototype of an intelligent next generation spatial information system. Deep Map realizes the vision of a future tourist guidance system that works as a mobile guide and as a web-based planning tool. This long term research project addresses several challenging research aspects covering intelligent integration of information from different data sources and services including geographical information systems, multi-media databases, and interactive internet data sources such as reservation systems. On the basis of this complex information system the European Media Laboratory plans to build user interfaces that allow intuitive and easy access to information. Such a user interface including visual and natural language processing will be included into a mobile device that navigates the user through a city. Also virtual tours in a 3D-reconstructed city will be possible. In this paper, we present the current system that already covers a number of these aspects and demonstrates how tourists may be guided in the future. Navigation services communicate optimal routes to users by providing sequences of instructions for these routes. Each single instruction guides the wayfinder from one decision point to the next. The instructions are based on geometric data from the street network, which is typically the only dataset available. This paper addresses the question of enriching such wayfinding instructions with local landmarks. We propose measures to formally specify the landmark saliency of a feature. Values for these measures are subject to hypothesis tests in order to define and extract landmarks from datasets. The extracted landmarks are then integrated in the wayfinding instructions. A concrete example from the city of Vienna demonstrates the applicability and usefulness of the method. Low-vision and blind bus riders often rely on known physical landmarks to help locate and verify bus stop locations (e.g., by searching for a shelter, bench, newspaper bin). However, there are currently few, if any, methods to determine this information a priori via computational tools or services. In this paper, we introduce and evaluate a new scalable method for collecting bus stop location and landmark descriptions by combining online crowdsourcing and Google Street View (GSV). We conduct and report on three studies in particular: (i) a formative interview study of 18 people with visual impairments to inform the design of our crowdsourcing tool; (ii) a comparative study examining differences between physical bus stop audit data and audits conducted virtually with GSV; and (iii) an online study of 153 crowd workers on Amazon Mechanical Turk to examine the feasibility of crowdsourcing bus stop audits using our custom tool with GSV. Our findings reemphasize the importance of landmarks in non-visual navigation, demonstrate that GSV is a viable bus stop audit dataset, and show that minimally trained crowd workers can find and identify bus stop landmarks with 82.5 accuracy across 150 bus stop locations (87.3 with simple quality control). We present a system for the real-time generation of car navigation instructions with landmarks. Our system relies exclusively on freely available map data from OpenStreetMap, organizes its output to fit into the available time until the next driving maneuver, and reacts in real time to driving errors. We show that female users spend significantly less time looking away from the road when using our system compared to a baseline system. An image analysis scheme can automate the detection of landmarks and events in large image collections, significantly improving the content-consumption experience. Pedestrian-navigation services enable people to retrieve precise instructions to reach a specific location. However, the development of mobile spatial-information technologies for pedestrians is still at the beginning and faces several difficulties. As the spatial behavior of people on foot differs in many ways from the driver's performance, common concepts for car-navigation services are not suitable for pedestrian navigation. Particularly, the usage of landmarks is vitally important in human navigation. This contribution points out the main requirements for pedestrian-navigation technologies and presents an approach to identify pedestrian flows and to imply landmark information into navigation services for pedestrians