Familiarity-dependent computational modelling of indoor landmark selection for route communication: a ranking approach

Landmarks play key roles in human wayfinding and mobile navigation systems. Existing computational landmark selection models mainly focus on outdoor environments, and aim to identify suitable landmarks for guiding users who are unfamiliar with a particular environment, and fail to consider familiar users. This study proposes a familiarity-dependent computational method for selecting suitable landmarks for communicating with familiar and unfamiliar users in indoor environments. A series of salience measures are proposed to quantify the characteristics of each indoor landmark candidate, which are then combined in two LambdaMART-based learning-to-rank models for selecting landmarks for familiar and unfamiliar users, respectively. The evaluation with labelled landmark preference data by human participants shows that people’s familiarity with environments matters in the computational modelling of indoor landmark selection for guiding them. The proposed models outperform state-of-the-art models, and achieve hit rates of 0.737 and 0.786 for familiar and unfamiliar users, respectively. Furthermore, semantic relevance of a landmark candidate is the most important measure for the familiar model, while visual intensity is most informative for the unfamiliar model. This study enables the development of human-centered indoor navigation systems that provide familiarity-adaptive landmark-based navigation guidance

Computer models of saliency alone fail to predict subjective visual attention to landmarks during observed navigation

This study aimed to understand whether or not computer models of saliency could explain landmark saliency. An online survey was conducted and participants were asked to watch videos from a spatial navigation video game (Sea Hero Quest). Participants were asked to pay attention to the environments within which the boat was moving and to rate the perceived saliency of each landmark. In addition, state-of-the-art computer saliency models were used to objectively quantify landmark saliency. No significant relationship was found between objective and subjective saliency measures. This indicates that during passive observation of an environment while being navigated, current automated models of saliency fail to predict subjective reports of visual attention to landmarks

How Subdimensions of Salience Influence Each Other. Comparing Models Based on Empirical Data

Theories about salience of landmarks in GIScience have been evolving for about 15 years. This paper empirically analyses hypotheses about the way different subdimensions (visual, structural, and cognitive aspects, as well as prototypicality and visibility in advance) of salience have an impact on each other. The analysis is based on empirical data acquired by means of an in-situ survey (360 objects, 112 participants). It consists of two parts: First, a theory-based structural model is assessed using variance-based Structural Equation Modeling. The results achieved are, second, corroborated by a data-driven approach, i.e. a tree-augmented naive Bayesian network is learned. This network is used as a structural model input for further analyses. The results clearly indicate that the subdimensions of salience influence each other

Overcoming Spatial Deskilling Using Landmark-Based Navigation Assistance Systems

Abstract Background The repeated use of navigation assistance systems leads to decreased spatial orienting abilities. Previous studies demonstrated that augmentation of landmarks using auditory navigation instructions can improve incidental spatial learning when driving on a single route through an unfamiliar environment. Objective Based on these results, a series of experiments was conducted to further investigate both the impairment of spatial knowledge acquisition by standard navigation instructions and the positive impact of landmark augmentation in auditory navigation instructions on incidental spatial learning. Method The first Experiment replicated the previous setup in a driving simulator without additional visual route indicators. In a second experiment, spatial knowledge was tested after watching a video depicting assisted navigation along a real-world urban route. Finally, a third Experiment investigated incidental spatial knowledge acquisition when participants actively navigated through an unrestricted real-world,urban environment. Results All three experiments demonstrated better cued-recall performance for participants navigating with landmark-based auditory navigation instructions as compared to standard instructions. Notably, standard instructions were associated with reduced learning of landmarks at navigation relevant intersections as compared to landmarks alongside straight segments and the recognition of novel landmarks. Conclusion The results revealed a suppression of spatial learning by established navigation instructions, which were overcome by landmark-based navigation instructions. This emphasizes the positive impact of auditory landmark augmentation on incidental spatial learning and its generalizability to real-life settings. Application This research is paving the way for navigation assistants that, instead of impairing orienting abilities, incidentally foster spatial learning during every-day navigation. Précis This series of three experiments replicates the suppression of spatial learning by standard navigation instructions and the positive impact of landmark augmentation in auditory navigation instructions on incidental spatial learning during assisted navigation. Three experiments with growing degree of realism revealed the applicability and generalizability to real-life settings

The relationship between wayfinding performance, spatial layout and landmarks in virtual environments

Environmental factors, including landmarks that affect people’s wayfinding performance in unfamiliar environments have been discussed in a great number of studies. However, there is still no consensus on the factors that shape people’s performance or what makes a landmark preferable during wayfinding. Hence, this study aims to understand the impact of different spatial layouts, environmental conditions and landmarks on people’s wayfinding performance, and the factors that make landmarks salient. Sea Hero Quest (SHQ), an online game that has been played by more than 4.3 million people from 2016 to date, is selected as a case study to investigate the impact of different environments and other factors, in particular landmarks. Forty-five wayfinding levels of SHQ are analysed and compared using Geographic Information System (GIS) and Space syntax axial, segment and visibility graph analyses. A cluster analysis is conducted to examine the relationship between levels. Varying conditions associated with landmarks, weather and maps were taken into consideration. In order to investigate the process of selecting landmarks, visual, structural (whether landmarks are global or local) and cognitive saliency are analysed using web-based surveys, saliency algorithms and the visibility of landmarks. Results of this study show that the complexity of layouts plays a major role in wayfinding; as the complexity of layout increases, so does the time taken to complete the wayfinding task. Similarly, the weather condition has an effect; as the weather becomes foggy and visibility decreases, the time taken to complete the wayfinding task increases. It is discovered that landmarks that are visible for more than 25% of a journey can be defined as global landmarks whereas the rest can be defined as local landmarks. Findings also show that landmarks that are visually salient (objects with a unique colour and size) and structurally salient (objects that are closer to people) are registered more by people in unfamiliar environments. This study contributes to the existing literature by exploring the factors that affect people’s wayfinding performance by using the largest dataset in the field (so providing more accurate results), focusing on 45 different layouts (while current research studies mostly focus on one or two different layouts), by proposing a threshold to distinguish global and local landmarks, and analysing visual, structural and cognitive saliency through various measures

Aspek-Aspek Arsitektur Tradisional dalam Landmark di Kota-Kota Besar di Indonesia

Penelitian ini meninjau aspek-aspek arsitektur tradisional yang menjadi landmark di kota-kota besar di Indonesia dan popularitasnya dibandingkan landmark yang sepenuhnya modern dengan memeriksa jenis, aspek, kategori, dan daya tarik landmark di ibukota provinsi di Indonesia. Analisis deskriptif dan one-way ANOVA digunakan untuk mengklasifikasikan landmark dan memeriksa secara kuantitatif relasi antara landmark dengan daya tariknya bagi masyarakat kota. Hasil mengungkapkan bahwa 39 dari 121 landmark yang disurvai memiliki aspek arsitektur tradisional. Arsitektur tradisional dapat dilihat dalam aspek atap, bangunan, dan ornamen. Kategori landmark yang mengandung aspek arsitektur tradisional adalah masjid, museum, taman, jalan, kompleks bangunan, kuil/wihara/kelenteng, pura, benteng, keraton, monumen, dan pasar. Hasil analisis ANOVA menunjukkan bahwa landmark yang mengandung aspek arsitektur dan menonjol secara visual memiliki daya tarik lebih tinggi dari landmark yang hanya mengandung salah satu karakteristik tersebut. Hasil ini meningkatkan pemahaman mengenai pentingnya aspek arsitektur tradisional untuk diterapkan dalam desain landmark dan menyarankan penambahan aspek-aspek arsitektur tradisional pada landmark yang telah ada maupun pada desain landmark yang akan datan

DYNAMICS OF COLLABORATIVE NAVIGATION AND APPLYING DATA DRIVEN METHODS TO IMPROVE PEDESTRIAN NAVIGATION INSTRUCTIONS AT DECISION POINTS FOR PEOPLE OF VARYING SPATIAL APTITUDES

Cognitive Geography seeks to understand individual decision-making variations based on fundamental cognitive differences between people of varying spatial aptitudes. Understanding fundamental behavioral discrepancies among individuals is an important step to improve navigation algorithms and the overall travel experience. Contemporary navigation aids, although helpful in providing turn-by-turn directions, lack important capabilities to distinguish decision points for their features and importance. Existing systems lack the ability to generate landmark or decision point based instructions using real-time or crowd sourced data. Systems cannot customize personalized instructions for individuals based on inherent spatial ability, travel history, or situations. This dissertation presents a novel experimental setup to examine simultaneous wayfinding behavior for people of varying spatial abilities. This study reveals discrepancies in the information processing, landmark preference and spatial information communication among groups possessing differing abilities. Empirical data is used to validate computational salience techniques that endeavor to predict the difficulty of decision point use from the structure of the routes. Outlink score and outflux score, two meta-algorithms that derive secondary scores from existing metrics of network analysis, are explored. These two algorithms approximate human cognitive variation in navigation by analyzing neighboring and directional effect properties of decision point nodes within a routing network. The results are validated by a human wayfinding experiment, results show that these metrics generally improve the prediction of errors. In addition, a model of personalized weighting for users\u27 characteristics is derived from a SVMrank machine learning method. Such a system can effectively rank decision point difficulty based on user behavior and derive weighted models for navigators that reflect their individual tendencies. The weights reflect certain characteristics of groups. Such models can serve as personal travel profiles, and potentially be used to complement sense-of-direction surveys in classifying wayfinders. A prototype with augmented instructions for pedestrian navigation is created and tested, with particular focus on investigating how augmented instructions at particular decision points affect spatial learning. The results demonstrate that survey knowledge acquisition is improved for people with low spatial ability while decreased for people of high spatial ability. Finally, contributions are summarized, conclusions are provided, and future implications are discussed

Quantifying mutual-understanding in dialogue

PhDThere are two components of communication that provide a natural index of mutual-understanding in dialogue. The first is Repair; the ways in which people detect and deal with problems with understanding. The second is Ellipsis/Anaphora; the use of expressions that depend directly on the accessibility of the local context for their interpretation. This thesis explores the use of these two phenomena in systematic comparative analyses of human-human dialogue under different task and media conditions. In order to do this it is necessary to a) develop reliable, valid protocols for coding the different Repair and Ellipsis/Anaphora phenomena b) establish their baseline patterns of distribution in conversation and c) model their basic statistical inter-relationships and their predictive value. Two new protocols for coding Repair and Ellipsis/Anaphora phenomena are presented and applied to two dialogue corpora, one of ordinary 'everyday' conversations and one of task-oriented dialogues. These data illustrate that there are significant differences in how understanding is created and negotiated across conditions. Repair is shown to be a ubiquitous feature in all dialogue. The goals of the speaker directly affect the type of Repair used. Giving instructions leads to a higher rate of self-editing; following instructions increases corrections and requests for clarification. Medium and familiarity also influence Repair; when eye contact is not possible there are a greater number of repeats and clarifications. Anaphora are used less frequently in task-oriented dialogue whereas types of Ellipsis increase. The use of Elliptical phrases that check, confirm or acknowledge is higher when there is no eye contact. Familiar pairs use more elliptical expressions, especially endophora and elliptical questions. Following instructions leads to greater use of elliptical (non-sentential) phrases. Medium, task and social norms all have a measureable effect on the components of dialogue that underpin mutual-understanding

Computer models of saliency alone fail to predict subjective visual attention to landmarks during observed navigation

In this study, it was aimed to understand whether or not computer models of saliency could explain landmark saliency. An online survey was conducted and participants were asked to watch videos from a spatial navigation video game (Sea Hero Quest). Participants were asked to pay attention to the environments within which the boat was moving and to rate the perceived saliency of each landmark. In addition, state-of-the-art computer saliency models were used to objectively quantify landmark saliency. No significant relationship was found between objective and subjective saliency measures. This indicates that during passive observation of an environment being navigated, current automated models of saliency fail to predict subjective reports of visual attention to landmarks

Assessing landmark salience for human navigation

Prominent spatial features play an important role for a plethora of spatially related tasks, including spatial learning, wayfinding and navigation, and the communication of route directions. Human judgment of the prominence or importance of these spatial features, for which the term landmark became popular, is typically based on subjective impressions and experience. The computational assessment of the prominence of these spatial objects is of interest to various scientific disciplines and applications, including spatially related information and navigation systems. Computational salience assessment, however, is highly challenging, as information systems need objective criteria and formalized techniques to reproduce human judgment of landmark salience. We propose a conceptual framework for assessing the salience of landmarks for navigation. Landmark salience is derived as a result of the observer's point of view, both physical and cognitive, the surrounding environment, and the objects contained therein. This is in contrast to the currently held view that salience is an inherent property of some spatial feature. Salience, in our approach, is expressed as a three-valued Saliency Vector. The components that determine this vector are Perceptual Salience, which defines the exogenous (or passive) potential of an object or region for acquisition of visual attention, Cognitive Salience, which is an endogenous (or active) mode of orienting attention, triggered by informative cues providing advance information about the target location, and Contextual Salience, which is tightly coupled to modality and task to be performed. This separation between voluntary and involuntary direction of visual attention in dependence of the context allows defining a framework that accounts for the interaction between observer, environment, and landmark. We identify the low-level factors that contribute to each type of salience and suggest a probabilistic approach for their integration. The framework serves as a bridge between findings from spatial cognition research and practical applications, and forms the basis for a computational model, which is used as test-bed for the evaluation of the concepts and methods developed within the scope of this work. The evaluation includes a comparison with human assessment of salience and provides the evidence for assessing the quality of the model. The results of this comparison suggest that the conceptual framework provides reasonably accurate assessments of saliency for perceptually distinct objects, but also identifies two major issues. The first relates to a systematic weighting issue of low-level components due to the proposed technique for the integrated saliency assessment, and the second aspect is the indication that the model lacks explanatory power due to the limited number of low- level components, in particular for cognitive components.Prominente räumliche Objekte spielen eine wichtig Rolle bei einer Vielzahl von raumbezogenen Aufgaben, wie zum Beispiel beim Erlernen der räumlichen Umgebung, bei der Wegfindung und Navigation, oder auch bei der Kommunikation von Routenbeschreibungen. Menschen beurteilen die Prominenz solcher Objekte, welche oft auch als Landmarken bezeichnet werden, aufgrund subjektiver Eindrücke und Erfahrungen. Die automatische Abschätzung dieser Prominenz mithilfe von Berechnungsmodelle und Algorithmen ist ausschlaggebend für die Entwicklung und Implementierung von Informationssystemen der nächsten Generation. Allerdings ist diese automatische Abschätzung sehr komplex und anspruchsvoll, da Informationssysteme weder subjektive Eindrücke verarbeiten noch über Erfahrungen verfügen, sondern auf formalisierte Methoden und Techniken angewiesen sind. Diese Dissertation befasst sich mit den konzeptuellen Rahmenbedingungen die zu einer akkuraten automatischen Abschätzung der Prominenz von Landmarken notwendig sind, wobei Prominenz als Salienz verstanden wird, also das Hervorspringen oder Hervorstehen eines Objekts aus einer Gruppe von Objekten. Die Salienz von räumlichen Objekten ist abgeleitet von drei zentralen Komponenten, nämlich 1) vom physischen und kognitivem Standpunkt des Beobachters, 2) von den Gegebenheiten der räumlichen Umgebung, und 3) von den einzelnen Objekten die sich im Wahrnehmungsbereich des Beobachters befinden. Die Salienz ist dementsprechend als drei-dimensionaler Vektor definiert, bestehend aus einer Wahrnehmungskomponente, einer Kognitionskomponente, und einer Kontextkomponente. Der konzeptuelle Rahmen diente dazu, Forschungsresultate aus verschiedenen wissenschaftlichen Disziplinen zu integrieren und ein Berechnungsmodel und Prototyp zu erstellen, welches als Testumgebung für die Evaluierung der angewandten Konzepte und Methoden, sowie für weitere Forschungsprojekte benutzt werden kann. Die Evaluierung besteht aus einem Vergleich der Resultate mit den Resultaten einer entsprechenden Umfrage und dient dazu, die Qualität des Berechnungsmodels abzuschätzen. Die Ergebnisse der Evaluierung zeigen dass der konzeptuelle Rahmen und das Berechnungsmodell tendenziell korrekte Abschätzungen der Salienz von Landmarken produzieren. Die Ergebnisse zeigen aber auch auf dass das Model Schwächen und Lücken hat, vor allem in Bezug auf die einzelnen Komponenten die zur Salienz beitragen