Automatic generation of tourist maps

Tourist maps are essential resources for visitors to an unfamiliar city because they visually highlight landmarks and other points of interest. Yet, hand-designed maps are static representations that cannot adapt to the needs and tastes of the individual tourist. In this paper we present an automated system for designing tourist maps that selects and highlights the information that is most important to tourists. Our system determines the salience of map elements using bottom-up vision-based image analysis and top-down web-based information extraction techniques. It then generates a map that emphasizes the most important elements, using a combination of multiperspective rendering to increase visibility of streets and landmarks, and cartographic generalization techniques such as simplification, deformation, and displacement to emphasize landmarks and de-emphasize less important buildings. We show a number of automatically generated tourist maps of San Francisco and compare them to existing automated and manual approaches. © 2008 ACM

Time Travelers: Mapping Museum Visitors across Time and Space

Open-air museums may encompass structures, buildings, sites, and other types of objects and artifacts that span across space and, because these objects were built and/or used during multiple periods of significance, across time. The multiplicity of storylines can confuse visitors. Thus, this paper introduces Somewhere in Time, a novel installation that integrates a combination of technologies with historic content that allows users to explore both time and space across museum structures/sites. We describe our work conceptualizing and designing a personalized, interactive map (Time Travelers) that allows visitors to explore complex narratives across both time and space

Mapping the ‘Magic of Huesca’: a methodological proposal for the design of tourist cartography

Maps published for tourism promotion and information constitute a particular sub-group of tourist maps made using Geographic Information Systems (GIS). This paper proposes a methodological protocol for the systematic elaboration of tourism mapping. This procedure is applied to the design of the tourist map of the province of Huesca. The cartographic tool was designed based on the needs of tourism promotion by the Public Administration, seeking the halfway point between persuasion and precision when representing elements on the map. Given that data would have to be updated and modified in the future, open-source software was used so that the administration can then run, modify and update it. In addition, the project was validated through surveys to two different audiences (general and expert)

Map Style Formalization: Rendering Techniques Extension for Cartography

International audienceCartographic design requires controllable methods and tools to produce maps that are adapted to users' needs and preferences. The formalized rules and constraints for cartographic representation come mainly from the conceptual framework of graphic semiology. Most current Geographical Information Systems (GIS) rely on the Styled Layer Descriptor and Semiology Encoding (SLD/SE) specifications which provide an XML schema describing the styling rules to be applied on geographic data to draw a map. Although this formalism is relevant for most usages in cartography, it fails to describe complex cartographic and artistic styles. In order to overcome these limitations, we propose an extension of the existing SLD/SE specifications to manage extended map stylizations, by the means of controllable expressive methods. Inspired by artistic and cartographic sources (Cassini maps, mountain maps, artistic movements, etc.), we propose to integrate into our system three main expressive methods: linear stylization, patch-based region filling and vector texture generation. We demonstrate how our pipeline allows to personalize map rendering with expressive methods in several examples

Translated visual guides aimed at tourists: conveyance of the visual text

This paper deals with the translation of the touristic website genre and more specifically the localisation of websites. Furthermore, the use of maps online as multilingual guides to attract visitors will be discussed. Six websites, one per each of the most important cities of the Autonomous Community of Galicia have been analysed. This study will reveal how several linguistic and visual strategies were involved in the creation of colourful, inspiring,and relaxing websites which make use of concise texts and maps throughout some of the thematic links. Also, the role of public institutions will be accounted for since these have a major impact on the resources available to create these visual guides and promote touristic destinations.Esta investigação visa apresentar uma análise da tradução do género “site turístico” e, em particular, da localização de websites. Abordará também o uso de mapas multilingues em Internet como guia e ferramenta para a atração de turistas. O trabalho analisa seis websites que descrevem seis das cidades mais importantes da Galiza e mostra os esforços linguísticos e visuais para criar uma serie de sites coloridos, inspiradores e relaxantes com textos concisos e mapas que se distribuem ao longo de algumas das ligações temáticas. Finalmente, destaca o papel das instituições públicas enquanto entidades que distribuem os recursos disponíveis para criar estes guias e promover os diferentes destinos turísticos.info:eu-repo/semantics/publishedVersio

Automatic Understanding and Mapping of Regions in Cities Using Google Street View Images

The use of semantic representations to achieve place understanding has been widely studied using indoor information. This kind of data can then be used for navigation, localization, and place identification using mobile devices. Nevertheless, applying this approach to outdoor data involves certain non-trivial procedures, such as gathering the information. This problem can be solved by using map APIs which allow images to be taken from the dataset captured to add to the map of a city. In this paper, we seek to leverage such APIs that collect images of city streets to generate a semantic representation of the city, built using a clustering algorithm and semantic descriptors. The main contribution of this work is to provide a new approach to generate a map with semantic information for each area of the city. The proposed method can automatically assign a semantic label for the cluster on the map. This method can be useful in smart cities and autonomous driving approaches due to the categorization of the zones in a city. The results show the robustness of the proposed pipeline and the advantages of using Google Street View images, semantic descriptors, and machine learning algorithms to generate semantic maps of outdoor places. These maps properly encode the zones existing in the selected city and are able to provide new zones between current ones.This work has been supported by the Spanish Grant PID2019-104818RB-I00 funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”. José Carlos Rangel and Edmanuel Cruz were supported by the Sistema Nacional de Investigación (SNI) of SENACYT, Panama

Generation and Validation of Workflows for On-demand Mapping

The paper presents a method to automatically select and sequence the tasks required to build maps according to user requirements. Workflows generated are analysed using Petri nets to assess their validity before execution. Although further work is required to select the optimal method for generating the workflow and to execute the workflow, the proposed method can be used on any workflow to assess its validity

Talk2Nav: Long-Range Vision-and-Language Navigation with Dual Attention and Spatial Memory

The role of robots in society keeps expanding, bringing with it the necessity of interacting and communicating with humans. In order to keep such interaction intuitive, we provide automatic wayfinding based on verbal navigational instructions. Our first contribution is the creation of a large-scale dataset with verbal navigation instructions. To this end, we have developed an interactive visual navigation environment based on Google Street View; we further design an annotation method to highlight mined anchor landmarks and local directions between them in order to help annotators formulate typical, human references to those. The annotation task was crowdsourced on the AMT platform, to construct a new Talk2Nav dataset with $10,714$ routes. Our second contribution is a new learning method. Inspired by spatial cognition research on the mental conceptualization of navigational instructions, we introduce a soft dual attention mechanism defined over the segmented language instructions to jointly extract two partial instructions -- one for matching the next upcoming visual landmark and the other for matching the local directions to the next landmark. On the similar lines, we also introduce spatial memory scheme to encode the local directional transitions. Our work takes advantage of the advance in two lines of research: mental formalization of verbal navigational instructions and training neural network agents for automatic way finding. Extensive experiments show that our method significantly outperforms previous navigation methods. For demo video, dataset and code, please refer to our project page: https://www.trace.ethz.ch/publications/2019/talk2nav/index.htmlComment: 20 pages, 10 Figures, Demo Video: https://people.ee.ethz.ch/~arunv/resources/talk2nav.mp

3D Generalisation of Building Components – An Initial Proof of Concept

A varied range of applications make use of 3D models nowadays, for instance in urban planning, energy demand studies, solar irradiation, or noise estimation. Acquisition, maintenance, and production of 3D spatial data is costly and laborious, especially at a national level, a great challenge for National Mapping and Cadastral Agencies (NMCAs) – such as Ordnance Survey (OS) for Great Britain. Generalisation is designed to address this challenge, where new datasets are created from a single source by the selection of the desired information and reduction of the amount of detail and data volume. Extensive literature exists in the context of 2D generalisation and automated algorithms exist to remove unwanted detail, however, adding a third dimension complicates the process significantly. Here, a methodology to address this issue is proposed, where the façades of a 3D building are decomposed, rotated, and translated from 3D environment to 2D. Existing automated 2D generalisation operators are applied to building elements and once generalised, they are rotated back to 3D. The outer shell of the resulting generalised 3D building is reconstructed with the independently generalised façade. The results demonstrate a potential flexible, component-based method for 3D generalisation, that could benefit NMCAs