A framework for assessing the salience of landmarks for wayfinding tasks

Background: Landmarks play an important role for the understanding of and reasoning about physical large-scale space. Landmarks also play an important role in every day's spatial tasks, such as wayfinding and navigation. The property of being a landmark has so far been attributed to distinct objects, which are either well known or otherwise unique, such as the Eiffel Tower or a lighthouse on the shore. We propose a framework for the assessment of the landmarkedness of potential landmarks for wayfinding tasks, which is based on the relationship between observer, physical environment, and potential landmark. We argue that landmarkedness is not an inherent property of some object, but rather the result of this tri-lateral configuration. The basic idea is to use this configuration to define the individual components that contribute to the total salience of the object and integrate them in a coherent framework. Main contribution: The framework considers two types of salience: (1) the perceptual salience, which describes the potential of a feature to capture a wayfinder's attention (attentional capture), and (2) the cognitive salience, which explains how strong attention is guided by the wayfinder (attentional orienting). The assessment of the perceptual salience is based on the salience of the incoming stimuli, the perceived concepts, and the spatial layout of a scene, while the cognitive salience considers the subjective importance of the object with respect to the individual's context and knowledge. The most general requirement of a landmark is that it must be salient in some sense. This requires that it contrast with the environment, either in terms of its attributes (i.e., color, texture, etc.), the status of the perceived concept (i.e., church or commercial building), or due to its spatial location with respect to the other objects in the scene (i.e., in the middle of town). Such contrast, however, is only perceivable if the potential landmark is visible from the observer's current point of view. Therefore, for assessing an object's landmarkedness, we consider attributes, objects, and relations to other visible objects only. In addition to the salience perceived from physical contrast, the cognitive abilities of the observer play an important role in selecting appropriate objects for reference. This subjective selection implies that the context, together with our knowledge, thoughts and preconceptions shape what we perceive and finally select as reference for making decisions, which directly influences the assessment of the relative importance or salience of potential landmarks. The assessment of the salience of potential landmarks, hence, needs to consider cognitive aspects, along with the perceptual stimuli. Implications: The accurate assessment of the relative importance of geographic objects is a crucial aspect of many wayfinding-related tools and applications, such as route generation and description algorithms, navigation systems, or location-based services. The integration of appropriate landmarks in such applications decreases the cognitive load put on the wayfinder, and hence increases efficiency and reliability of the application

On the assessment of landmark salience for human navigation

In this paper, 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. Finally, we discuss the implications, consider restrictions, and explore the scope of the framewor

Computational Challenges in Cooperative Intelligent Urban Transport

This report documents the talks and group work of Dagstuhl Seminar 16091 “Computational Challenges in Cooperative Intelligent Urban Transport”. This interdisciplinary seminar brought researchers together from many fields including computer science, transportation, operations research, mathematics, machine learning and artificial intelligence. The seminar included two formats of talks: several minute research statements and longer overview talks. The talks given are documented here with abstracts. Furthermore, this seminar consisted of significant amounts of group work that is also documented with short abstracts detailing group discussions and planned outcomes

Estimating Moving Regions out of Point Data – from Excavation Sites in the Amazon region to Areas of Influence of Prehistoric Cultures

Ponencias, comunicaciones y pósters presentados en el 17th AGILE Conference on Geographic Information Science "Connecting a Digital Europe through Location and Place", celebrado en la Universitat Jaume I del 3 al 6 de junio de 2014.How can we derive the changing area of influence of specific cultures from only a few excavation sites in the Amazon region? The approach used for calculating areas of influence for several time intervals strongly depends on the kind of available input data and the examined issues. Our approach divides the input point data into different time intervals and calculates an area (or areas) of influence for each, factoring in spatial and temporal uncertainties inherent in the data. The computation is based on a cost surface, which is derived from the needs and capabilities of the analyzed prehistoric culture or tradition. To take into ac-count that archaeological data is inherently vague, the database is able to handle spatial uncertainties by applying varying maximum distances. Based on the cost raster and the maximum distance a maximum cost value is calculated which is used to derive the said area(s) of influence, which can then be analyzed for changes

Classroom Civility

Learning is a sacred, lifelong process that commences in the classroom. We need classrooms that provide safe places to learn. As a student, I urge my classmates to recognize that now is the time to begin debating and discussing that which is hard and controversial. To do this well, both students and professors must work together to form communities built on respect that encourage questioning. Posting by a college student about civility in the classroom from In All Things - an online hub committed to the claim that the life, death, and resurrection of Jesus Christ has implications for the entire world. http://inallthings.org/classroom-civility

Semantic identification of urban green spaces: forest

Urban Green Spaces (UGSs) are recognized as crucial parts of the human-nature ecosystem in densely populated urban centers. Even though they have been intensively studied, an ultimate list of all types of UGSs in Europe still does not exist. This challenges decision making on whether an area should be considered an UGS or belong to another land-use class. Furthermore, the means of precise identification of UGSs are dependent, among others, on their type and semantics. Therefore, in this paper, we investigate forests as UGSs and automatically identify them using their distinct characteristics from Sentinel-2 images as well as descriptive properties derived from them, i.e., vegetation indices and texture metrics.We enrich these properties with forest relevant features such as minimum vegetation height and homogeneity. To assess the reliability of the proposed workflow, we test our approach in two German cities and compare the results with existing governmental land use data sets. With the implemented approach we precisely identify over 90% of the existing forests in the study areas. The main restriction of the approach is the transferability of the thresholds of predictor variables such as homogeneity and dissimilarity

Towards an ontology of urban green spaces

Growing interest in Urban Green Spaces (UGSs) has led to the discovery of the wide spectrum of services that they either provide or support. Yet, there is persistent confusion about what types of green spaces exist and how they should be classified. Current Land Use (LU) and Land Cover (LC) maps both at European and national levels are either lacking or misclassify various types of UGS, thus underestimating the actual amount of green space that a city offers its citizens. In this paper, we highlight reasons for green space misclassification and suggest an ontology of UGSs. Our ontology takes into account both the physical appearance of UGSs and their semantics. We characterize the physical appearance using four different LC classes while providing semantics using unique characteristics expressed as rules. The ontology proposed emphasizes in particular frequently disregarded small and heterogeneous UGSs and can be used as a basis for their precise identification and mapping

Prediction of landmarks using (personalised) decision trees

(VLID)452604

Classifying Urban Green Spaces using a combined Sentinel-2 and Random Forest approach

Environmental and human benefits of Urban Green Spaces (UGSs) have been known for a long time. However, the definition of a reasonable greening strategy still remains challenging due to the lack of sufficient baseline information as well as a lack of consensus what constitutes a UGS. Therefore, accurate identification of the existing green spaces in cities is crucial for developing UGS inventories for urban planning and resource management activities. In this paper we explore the potential of freely available highest resolution multi-spectral remote sensing imagery to identify large homogeneous as well small heterogeneous UGSs. The approach of using a Random Forest classification on Sentinel-2 imagery is shown to be useful to identify various UGSs with a 97 % accuracy. Freely available data and a relatively straightforward implementation of the proposed approach makes it a valuable tool for decision and policy makers