Georeferencing flickr resources based on textual meta-data

The task of automatically estimating the location of web resources is of central importance in location-based services on the Web. Much attention has been focused on Flickr photos and videos, for which it was found that language modeling approaches are particularly suitable. In particular, state-of-the art systems for georeferencing Flickr photos tend to cluster the locations on Earth in a relatively small set of disjoint regions, apply feature selection to identify location-relevant tags, then use a form of text classification to identify which area is most likely to contain the true location of the resource, and finally attempt to find an appropriate location within the identified area. In this paper, we present a systematic discussion of each of the aforementioned components, based on the lessons we have learned from participating in the 2010 and 2011 editions of MediaEval’s Placing Task. Extensive experimental results allow us to analyze why certain methods work well on this task and show that a median error of just over 1 km can be achieved on a standard benchmark test set

Georeferencing Flickr photos using language models at different levels of granularity: an evidence based approach

The topic of automatically assigning geographic coordinates to Web 2.0 resources based on their tags has recently gained considerable attention. However, the coordinates that are produced by automated techniques are necessarily variable, since not all resources are described by tags that are sufficiently descriptive. Thus there is a need for adaptive techniques that assign locations to photos at the right level of granularity, or, in some cases, even refrain from making any estimations regarding location at all. To this end, we consider the idea of training language models at different levels of granularity, and combining the evidence provided by these language models using Dempster and Shafer’s theory of evidence. We provide experimental results which clearly confirm that the increased spatial awareness that is thus gained allows us to make better informed decisions, and moreover increases the overall accuracy of the individual language models

Exploring place through user-generated content: Using Flickr tags to describe city cores

Terms used to describe city centers, such as Downtown, are key concepts in everyday or vernacular language. Here, we explore such language by harvesting georeferenced and tagged metadata associated with 8 million Flickr images and thus consider how large numbers of people name city core areas. The nature of errors and imprecision in tagging and georeferencing are quantified, and automatically generated precision measures appear to mirror errors in the positioning of images. Users seek to ascribe appropriate semantics to images, though bulk-uploading and bulk-tagging may introduce bias. Between 0.5--2% of tags associated with georeferenced images analyzed describe city core areas generically, while 70% of all georeferenced images analyzed include specific place name tags, with place names at the granularity of city names being by far the most common. Using Flickr metadata, it is possible not only to describe the use of the term Downtown across the USA, but also to explore the borders of city center neighborhoods at the level of individual cities, whilst accounting for bias by the use of tag profiles

Georeferencing textual annotations and tagsets with geographical knowledge and language models

Presentamos en este artículo cuatro aproximaciones al georeferenciado genérico de anotaciones textuales multilingües y etiquetas sem ánticas. Las cuatro aproximaciones se basan en el uso de 1) Conocimiento geogr áfi co, 2) Modelos del lenguaje (LM), 3) Modelos del lenguaje con predicciones re-ranking y 4) Fusi ón de las predicciones basadas en conocimiento geográfi co con otras aproximaciones. Los recursos empleados incluyen el gazetteer geogr áfi co Geonames, los modelos de recuperación de informaci ón TFIDF y BM25, el Hiemstra Language Modelling (HLM), listas de stop words para varias lenguas y un diccionario electróonico de la lengua inglesa. Los mejores resultados en precisión del georeferenciado se han obtenido con la aproximación de re-ranking que usa el HLM y con su fusióon con conocimiento geográfi co. Estas estrategias mejoran los mejores resultados de los mejores sistemas participantes en la tarea o cial de georeferenciado en MediaEval 2010. Nuestro mejor resultado obtiene una precisión de 68.53% en la tarea de geoeferenciado hasta 100 Km. This paper describes generic approaches for georeferencing multilingual textual annotations and sets of tags from metadata associated to textual or multimedia content with high precision. We present four approaches based on: 1) Geographical Knowledge, 2) Language Modelling (LM), 3) Language Modelling with Re-Ranking predictions, 4) Fusion of Geographical Knowledge predictions with the other approaches. The resources employed were the Geonames geographical gazetteer, the TFIDF and BM25 Information Retrieval algorithms, the Hiemstra Language Modelling (HLM) algorithm, stopwords lists from several languages, and an electronic English dictionary. The best results in georeferencing accuracy are achieved with the HLM Re-Ranking approach and its fusion with Geographical Knowledge. These strategies outperformed the best results in accuracy reported by the state-of-the art systems that participated at MediaEval 2010 official Placing task. Our best results achieved are 68.53% of accuracy georeferencing up to a distance of 100 Km.Postprint (author’s final draft

Investigating the feasibility of geo-tagged photographs as sources of land cover input data

Geo-tagged photographs are used increasingly as a source of Volunteered Geographic Information (VGI), which could potentially be used for land use and land cover applications. The purpose of this paper is to analyze the feasibility of using this source of spatial information for three use cases related to land cover: Calibration, validation and verification. We first provide an inventory of the metadata that are collected with geo-tagged photographs and then consider what elements would be essential, desirable, or unnecessary for the aforementioned use cases. Geo-tagged photographs were then extracted from Flickr, Panoramio and Geograph for an area of London, UK, and classified based on their usefulness for land cover mapping including an analysis of the accompanying metadata. Finally, we discuss protocols for geo-tagged photographs for use of VGI in relation to land cover applications

Spatially Ordered Treemaps

Existing treemap layout algorithms suffer to some extent from poor or inconsistent mappings between data order and visual ordering in their representation, reducing their cognitive plausibility. While attempts have been made to quantify this mismatch, and algorithms proposed to minimize inconsistency, solutions provided tend to concentrate on one-dimensional ordering. We propose extensions to the existing squarified layout algorithm that exploit the two-dimensional arrangement of treemap nodes more effectively. Our proposed spatial squarified layout algorithm provides a more consistent arrangement of nodes while maintaining low aspect ratios. It is suitable for the arrangement of data with a geographic component and can be used to create tessellated cartograms for geovisualization. Locational consistency is measured and visualized and a number of layout algorithms are compared. CIELab color space and displacement vector overlays are used to assess and emphasize the spatial layout of treemap nodes. A case study involving locations of tagged photographs in the Flickr database is described